diff --git a/benchmarks/benchmark_test.sh b/benchmarks/benchmark_test.sh deleted file mode 100644 index 29583677f0b226cfee0bdd14402000195bd1a741..0000000000000000000000000000000000000000 --- a/benchmarks/benchmark_test.sh +++ /dev/null @@ -1,33 +0,0 @@ -source benchmarks/train_loop.sh - -export WANDB_PROJECT_NAME="rl-algo-impls" -export VIRTUAL_DISPLAY=1 - -BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}" - -ALGOS=( - # "vpg" - "dqn" - # "ppo" -) -ENVS=( - # Basic - "CartPole-v1" - "MountainCar-v0" - # "MountainCarContinuous-v0" - "Acrobot-v1" - "LunarLander-v2" - # # PyBullet - # "HalfCheetahBulletEnv-v0" - # "AntBulletEnv-v0" - # "HopperBulletEnv-v0" - # "Walker2DBulletEnv-v0" - # # CarRacing - # "CarRacing-v0" - # Atari - "PongNoFrameskip-v4" - "BreakoutNoFrameskip-v4" - "SpaceInvadersNoFrameskip-v4" - "QbertNoFrameskip-v4" -) -train_loop "${ALGOS[*]}" "${ENVS[*]}" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD diff --git a/benchmarks/colab_atari1.sh b/benchmarks/colab_atari1.sh deleted file mode 100644 index ac3c646314420c360191f50135510c75e720a810..0000000000000000000000000000000000000000 --- a/benchmarks/colab_atari1.sh +++ /dev/null @@ -1,5 +0,0 @@ -source benchmarks/train_loop.sh -ALGOS="ppo" -ENVS="PongNoFrameskip-v4 BreakoutNoFrameskip-v4" -BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}" -train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD \ No newline at end of file diff --git a/benchmarks/colab_atari2.sh b/benchmarks/colab_atari2.sh deleted file mode 100644 index 5e7b79e72b626a8e93f635a45346670e07b20c34..0000000000000000000000000000000000000000 --- a/benchmarks/colab_atari2.sh +++ /dev/null @@ -1,5 +0,0 @@ -source benchmarks/train_loop.sh -ALGOS="ppo" -ENVS="SpaceInvadersNoFrameskip-v4 QbertNoFrameskip-v4" -BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}" -train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD \ No newline at end of file diff --git a/benchmarks/colab_basic.sh b/benchmarks/colab_basic.sh deleted file mode 100644 index affe598a08c94ea23cecacfab44931170ecfd29f..0000000000000000000000000000000000000000 --- a/benchmarks/colab_basic.sh +++ /dev/null @@ -1,5 +0,0 @@ -source benchmarks/train_loop.sh -ALGOS="ppo" -ENVS="CartPole-v1 MountainCar-v0 MountainCarContinuous-v0 Acrobot-v1 LunarLander-v2" -BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}" -train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD diff --git a/benchmarks/colab_benchmark.ipynb b/benchmarks/colab_benchmark.ipynb deleted file mode 100644 index a331841fd9fdd9b85c029c9c573652381d003483..0000000000000000000000000000000000000000 --- a/benchmarks/colab_benchmark.ipynb +++ /dev/null @@ -1,195 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "machine_shape": "hm", - "authorship_tag": "ABX9TyOGIH7rqgasim3Sz7b1rpoE", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "gpuClass": "standard", - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) in Google Colaboratory\n", - "## Parameters\n", - "\n", - "\n", - "1. Wandb\n", - "\n" - ], - "metadata": { - "id": "S-tXDWP8WTLc" - } - }, - { - "cell_type": "code", - "source": [ - "from getpass import getpass\n", - "import os\n", - "os.environ[\"WANDB_API_KEY\"] = getpass(\"Wandb API key to upload metrics, videos, and models: \")" - ], - "metadata": { - "id": "1ZtdYgxWNGwZ" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Setup\n", - "Clone [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) " - ], - "metadata": { - "id": "bsG35Io0hmKG" - } - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "!git clone https://github.com/sgoodfriend/rl-algo-impls.git" - ], - "metadata": { - "id": "k5ynTV25hdAf" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Installing the correct packages:\n", - "\n", - "While conda and poetry are generally used for package management, the mismatch in Python versions (3.10 in the project file vs 3.8 in Colab) makes using the package yml files difficult to use. For now, instead I'm going to specify the list of requirements manually below:" - ], - "metadata": { - "id": "jKxGok-ElYQ7" - } - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "!apt install python-opengl\n", - "!apt install ffmpeg\n", - "!apt install xvfb\n", - "!apt install swig" - ], - "metadata": { - "id": "nn6EETTc2Ewf" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "%cd /content/rl-algo-impls\n", - "!pip install -r colab_requirements.txt" - ], - "metadata": { - "id": "AfZh9rH3yQii" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Run Once Per Runtime" - ], - "metadata": { - "id": "4o5HOLjc4wq7" - } - }, - { - "cell_type": "code", - "source": [ - "import wandb\n", - "wandb.login()" - ], - "metadata": { - "id": "PCXa5tdS2qFX" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Restart Session beteween runs" - ], - "metadata": { - "id": "AZBZfSUV43JQ" - } - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "from pyvirtualdisplay import Display\n", - "\n", - "virtual_display = Display(visible=0, size=(1400, 900))\n", - "virtual_display.start()" - ], - "metadata": { - "id": "VzemeQJP2NO9" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "The below 5 bash scripts train agents on environments with 3 seeds each:\n", - "- colab_basic.sh and colab_pybullet.sh test on a set of basic gym environments and 4 PyBullet environments. Running both together will likely take about 18 hours. This is likely to run into runtime limits for free Colab and Colab Pro, but is fine for Colab Pro+.\n", - "- colab_carracing.sh only trains 3 seeds on CarRacing-v0, which takes almost 22 hours on Colab Pro+ on high-RAM, standard GPU.\n", - "- colab_atari1.sh and colab_atari2.sh likely need to be run separately because each takes about 19 hours on high-RAM, standard GPU." - ], - "metadata": { - "id": "nSHfna0hLlO1" - } - }, - { - "cell_type": "code", - "source": [ - "%cd /content/rl-algo-impls\n", - "os.environ[\"BENCHMARK_MAX_PROCS\"] = str(1) # Can't reliably raise this to 2+, but would make it faster.\n", - "!./benchmarks/colab_basic.sh\n", - "!./benchmarks/colab_pybullet.sh\n", - "# !./benchmarks/colab_carracing.sh\n", - "# !./benchmarks/colab_atari1.sh\n", - "# !./benchmarks/colab_atari2.sh" - ], - "metadata": { - "id": "07aHYFH1zfXa" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/benchmarks/colab_carracing.sh b/benchmarks/colab_carracing.sh deleted file mode 100644 index 7101140eba9fa2ee8b953ad07d7662fba47416f9..0000000000000000000000000000000000000000 --- a/benchmarks/colab_carracing.sh +++ /dev/null @@ -1,5 +0,0 @@ -source benchmarks/train_loop.sh -ALGOS="ppo" -ENVS="CarRacing-v0" -BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}" -train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD \ No newline at end of file diff --git a/benchmarks/colab_pybullet.sh b/benchmarks/colab_pybullet.sh deleted file mode 100644 index 69e36bac610748c5bd18f5a58a3e79bd39b8b181..0000000000000000000000000000000000000000 --- a/benchmarks/colab_pybullet.sh +++ /dev/null @@ -1,5 +0,0 @@ -source benchmarks/train_loop.sh -ALGOS="ppo" -ENVS="HalfCheetahBulletEnv-v0 AntBulletEnv-v0 HopperBulletEnv-v0 Walker2DBulletEnv-v0" -BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}" -train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD \ No newline at end of file diff --git a/benchmarks/train_loop.sh b/benchmarks/train_loop.sh deleted file mode 100644 index 7625bb7471b3c1e3dfdcb0558bbd2ed408a2564e..0000000000000000000000000000000000000000 --- a/benchmarks/train_loop.sh +++ /dev/null @@ -1,17 +0,0 @@ -train_loop () { - local WANDB_TAGS="benchmark_$(git rev-parse --short HEAD) host_$(hostname)" - local algo - local env - local seed - local WANDB_PROJECT_NAME="${WANDB_PROJECT_NAME:-rl-algo-impls-benchmarks}" - local args=() - (( VIRTUAL_DISPLAY == 1)) && args+=("--virtual-display") - local SEEDS="${SEEDS:-1 2 3}" - for algo in $(echo $1); do - for env in $(echo $2); do - for seed in $SEEDS; do - echo python train.py --algo $algo --env $env --seed $seed --pool-size 1 --wandb-tags $WANDB_TAGS --wandb-project-name $WANDB_PROJECT_NAME ${args[@]} - done - done - done -} \ No newline at end of file diff --git a/colab_enjoy.ipynb b/colab_enjoy.ipynb deleted file mode 100644 index 57daecf4ee497e489387bbb338cd2baa4b01a67a..0000000000000000000000000000000000000000 --- a/colab_enjoy.ipynb +++ /dev/null @@ -1,198 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "machine_shape": "hm", - "authorship_tag": "ABX9TyN6S7kyJKrM5x0OOiN+CgTc", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "gpuClass": "standard", - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) in Google Colaboratory\n", - "## Parameters\n", - "\n", - "\n", - "1. Wandb\n", - "\n" - ], - "metadata": { - "id": "S-tXDWP8WTLc" - } - }, - { - "cell_type": "code", - "source": [ - "from getpass import getpass\n", - "import os\n", - "os.environ[\"WANDB_API_KEY\"] = getpass(\"Wandb API key to upload metrics, videos, and models: \")" - ], - "metadata": { - "id": "1ZtdYgxWNGwZ" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "2. enjoy.py parameters" - ], - "metadata": { - "id": "ao0nAh3MOdN7" - } - }, - { - "cell_type": "code", - "source": [ - "WANDB_RUN_PATH=\"sgoodfriend/rl-algo-impls-benchmarks/rd0lisee\"" - ], - "metadata": { - "id": "jKL_NFhVOjSc" - }, - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Setup\n", - "Clone [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) " - ], - "metadata": { - "id": "bsG35Io0hmKG" - } - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "!git clone https://github.com/sgoodfriend/rl-algo-impls.git" - ], - "metadata": { - "id": "k5ynTV25hdAf" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Installing the correct packages:\n", - "\n", - "While conda and poetry are generally used for package management, the mismatch in Python versions (3.10 in the project file vs 3.8 in Colab) makes using the package yml files difficult to use. For now, instead I'm going to specify the list of requirements manually below:" - ], - "metadata": { - "id": "jKxGok-ElYQ7" - } - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "!apt install python-opengl\n", - "!apt install ffmpeg\n", - "!apt install xvfb\n", - "!apt install swig" - ], - "metadata": { - "id": "nn6EETTc2Ewf" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "%cd /content/rl-algo-impls\n", - "!pip install -r colab_requirements.txt" - ], - "metadata": { - "id": "AfZh9rH3yQii" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Run Once Per Runtime" - ], - "metadata": { - "id": "4o5HOLjc4wq7" - } - }, - { - "cell_type": "code", - "source": [ - "import wandb\n", - "wandb.login()" - ], - "metadata": { - "id": "PCXa5tdS2qFX" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Restart Session beteween runs" - ], - "metadata": { - "id": "AZBZfSUV43JQ" - } - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "from pyvirtualdisplay import Display\n", - "\n", - "virtual_display = Display(visible=0, size=(1400, 900))\n", - "virtual_display.start()" - ], - "metadata": { - "id": "VzemeQJP2NO9" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "%cd /content/rl-algo-impls\n", - "!python enjoy.py --wandb-run-path={WANDB_RUN_PATH}" - ], - "metadata": { - "id": "07aHYFH1zfXa" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/colab_requirements.txt b/colab_requirements.txt deleted file mode 100644 index 9dff0eb89e1d9019db82b09ceebb33fc636f4b55..0000000000000000000000000000000000000000 --- a/colab_requirements.txt +++ /dev/null @@ -1,9 +0,0 @@ -AutoROM.accept-rom-license >= 0.4.2, < 0.5 -stable-baselines3[extra] >= 1.7.0, < 1.8 -gym[box2d] >= 0.21.0, < 0.22 -pyglet == 1.5.27 -wandb >= 0.13.9, < 0.14 -pyvirtualdisplay == 3.0 -pybullet >= 3.2.5, < 3.3 -tabulate >= 0.9.0, < 0.10 -huggingface-hub >= 0.12.0, < 0.13 \ No newline at end of file diff --git a/colab_train.ipynb b/colab_train.ipynb deleted file mode 100644 index 7f981493d0545cfabd5b73db13f1f3bb30114855..0000000000000000000000000000000000000000 --- a/colab_train.ipynb +++ /dev/null @@ -1,200 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "machine_shape": "hm", - "authorship_tag": "ABX9TyMmemQnx6G7GOnn6XBdjgxY", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "gpuClass": "standard", - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) in Google Colaboratory\n", - "## Parameters\n", - "\n", - "\n", - "1. Wandb\n", - "\n" - ], - "metadata": { - "id": "S-tXDWP8WTLc" - } - }, - { - "cell_type": "code", - "source": [ - "from getpass import getpass\n", - "import os\n", - "os.environ[\"WANDB_API_KEY\"] = getpass(\"Wandb API key to upload metrics, videos, and models: \")" - ], - "metadata": { - "id": "1ZtdYgxWNGwZ" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "2. train run parameters" - ], - "metadata": { - "id": "ao0nAh3MOdN7" - } - }, - { - "cell_type": "code", - "source": [ - "ALGO = \"ppo\"\n", - "ENV = \"CartPole-v1\"\n", - "SEED = 1" - ], - "metadata": { - "id": "jKL_NFhVOjSc" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Setup\n", - "Clone [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) " - ], - "metadata": { - "id": "bsG35Io0hmKG" - } - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "!git clone https://github.com/sgoodfriend/rl-algo-impls.git" - ], - "metadata": { - "id": "k5ynTV25hdAf" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Installing the correct packages:\n", - "\n", - "While conda and poetry are generally used for package management, the mismatch in Python versions (3.10 in the project file vs 3.8 in Colab) makes using the package yml files difficult to use. For now, instead I'm going to specify the list of requirements manually below:" - ], - "metadata": { - "id": "jKxGok-ElYQ7" - } - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "!apt install python-opengl\n", - "!apt install ffmpeg\n", - "!apt install xvfb\n", - "!apt install swig" - ], - "metadata": { - "id": "nn6EETTc2Ewf" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "%cd /content/rl-algo-impls\n", - "!pip install -r colab_requirements.txt" - ], - "metadata": { - "id": "AfZh9rH3yQii" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Run Once Per Runtime" - ], - "metadata": { - "id": "4o5HOLjc4wq7" - } - }, - { - "cell_type": "code", - "source": [ - "import wandb\n", - "wandb.login()" - ], - "metadata": { - "id": "PCXa5tdS2qFX" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Restart Session beteween runs" - ], - "metadata": { - "id": "AZBZfSUV43JQ" - } - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "from pyvirtualdisplay import Display\n", - "\n", - "virtual_display = Display(visible=0, size=(1400, 900))\n", - "virtual_display.start()" - ], - "metadata": { - "id": "VzemeQJP2NO9" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "%cd /content/rl-algo-impls\n", - "!python train.py --algo {ALGO} --env {ENV} --seed {SEED}" - ], - "metadata": { - "id": "07aHYFH1zfXa" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/dqn/dqn.py b/dqn/dqn.py deleted file mode 100644 index 741208430475c92980a730e4f88332dfb6c5254b..0000000000000000000000000000000000000000 --- a/dqn/dqn.py +++ /dev/null @@ -1,182 +0,0 @@ -import copy -import numpy as np -import random -import torch -import torch.nn as nn -import torch.nn.functional as F - -from collections import deque -from torch.optim import Adam -from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs -from torch.utils.tensorboard.writer import SummaryWriter -from typing import List, NamedTuple, Optional, TypeVar - -from dqn.policy import DQNPolicy -from shared.algorithm import Algorithm -from shared.callbacks.callback import Callback -from shared.schedule import linear_schedule - - -class Transition(NamedTuple): - obs: np.ndarray - action: np.ndarray - reward: float - done: bool - next_obs: np.ndarray - - -class Batch(NamedTuple): - obs: np.ndarray - actions: np.ndarray - rewards: np.ndarray - dones: np.ndarray - next_obs: np.ndarray - - -class ReplayBuffer: - def __init__(self, num_envs: int, maxlen: int) -> None: - self.num_envs = num_envs - self.buffer = deque(maxlen=maxlen) - - def add( - self, - obs: VecEnvObs, - action: np.ndarray, - reward: np.ndarray, - done: np.ndarray, - next_obs: VecEnvObs, - ) -> None: - assert isinstance(obs, np.ndarray) - assert isinstance(next_obs, np.ndarray) - for i in range(self.num_envs): - self.buffer.append( - Transition(obs[i], action[i], reward[i], done[i], next_obs[i]) - ) - - def sample(self, batch_size: int) -> Batch: - ts = random.sample(self.buffer, batch_size) - return Batch( - obs=np.array([t.obs for t in ts]), - actions=np.array([t.action for t in ts]), - rewards=np.array([t.reward for t in ts]), - dones=np.array([t.done for t in ts]), - next_obs=np.array([t.next_obs for t in ts]), - ) - - def __len__(self) -> int: - return len(self.buffer) - - -DQNSelf = TypeVar("DQNSelf", bound="DQN") - - -class DQN(Algorithm): - def __init__( - self, - policy: DQNPolicy, - env: VecEnv, - device: torch.device, - tb_writer: SummaryWriter, - learning_rate: float = 1e-4, - buffer_size: int = 1_000_000, - learning_starts: int = 50_000, - batch_size: int = 32, - tau: float = 1.0, - gamma: float = 0.99, - train_freq: int = 4, - gradient_steps: int = 1, - target_update_interval: int = 10_000, - exploration_fraction: float = 0.1, - exploration_initial_eps: float = 1.0, - exploration_final_eps: float = 0.05, - max_grad_norm: float = 10.0, - ) -> None: - super().__init__(policy, env, device, tb_writer) - self.policy = policy - - self.optimizer = Adam(self.policy.q_net.parameters(), lr=learning_rate) - - self.target_q_net = copy.deepcopy(self.policy.q_net).to(self.device) - self.target_q_net.train(False) - self.tau = tau - self.target_update_interval = target_update_interval - - self.replay_buffer = ReplayBuffer(self.env.num_envs, buffer_size) - self.batch_size = batch_size - - self.learning_starts = learning_starts - self.train_freq = train_freq - self.gradient_steps = gradient_steps - - self.gamma = gamma - self.exploration_eps_schedule = linear_schedule( - exploration_initial_eps, - exploration_final_eps, - end_fraction=exploration_fraction, - ) - - self.max_grad_norm = max_grad_norm - - def learn( - self: DQNSelf, total_timesteps: int, callback: Optional[Callback] = None - ) -> DQNSelf: - self.policy.train(True) - obs = self.env.reset() - obs = self._collect_rollout(self.learning_starts, obs, 1) - learning_steps = total_timesteps - self.learning_starts - timesteps_elapsed = 0 - steps_since_target_update = 0 - while timesteps_elapsed < learning_steps: - progress = timesteps_elapsed / learning_steps - eps = self.exploration_eps_schedule(progress) - obs = self._collect_rollout(self.train_freq, obs, eps) - rollout_steps = self.train_freq - timesteps_elapsed += rollout_steps - for _ in range( - self.gradient_steps if self.gradient_steps > 0 else self.train_freq - ): - self.train() - steps_since_target_update += rollout_steps - if steps_since_target_update >= self.target_update_interval: - self._update_target() - steps_since_target_update = 0 - if callback: - callback.on_step(timesteps_elapsed=rollout_steps) - return self - - def train(self) -> None: - if len(self.replay_buffer) < self.batch_size: - return - o, a, r, d, next_o = self.replay_buffer.sample(self.batch_size) - o = torch.as_tensor(o, device=self.device) - a = torch.as_tensor(a, device=self.device).unsqueeze(1) - r = torch.as_tensor(r, dtype=torch.float32, device=self.device) - d = torch.as_tensor(d, dtype=torch.long, device=self.device) - next_o = torch.as_tensor(next_o, device=self.device) - - with torch.no_grad(): - target = r + (1 - d) * self.gamma * self.target_q_net(next_o).max(1).values - current = self.policy.q_net(o).gather(dim=1, index=a).squeeze(1) - loss = F.smooth_l1_loss(current, target) - - self.optimizer.zero_grad() - loss.backward() - if self.max_grad_norm: - nn.utils.clip_grad_norm_(self.policy.q_net.parameters(), self.max_grad_norm) - self.optimizer.step() - - def _collect_rollout(self, timesteps: int, obs: VecEnvObs, eps: float) -> VecEnvObs: - for _ in range(0, timesteps, self.env.num_envs): - action = self.policy.act(obs, eps, deterministic=False) - next_obs, reward, done, _ = self.env.step(action) - self.replay_buffer.add(obs, action, reward, done, next_obs) - obs = next_obs - return obs - - def _update_target(self) -> None: - for target_param, param in zip( - self.target_q_net.parameters(), self.policy.q_net.parameters() - ): - target_param.data.copy_( - self.tau * param.data + (1 - self.tau) * target_param.data - ) diff --git a/dqn/policy.py b/dqn/policy.py deleted file mode 100644 index 68c79e799172b0a40db56918b45550867f04e9f1..0000000000000000000000000000000000000000 --- a/dqn/policy.py +++ /dev/null @@ -1,37 +0,0 @@ -import numpy as np -import os -import torch - -from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs -from typing import Sequence, TypeVar - -from dqn.q_net import QNetwork -from shared.policy.policy import Policy - -DQNPolicySelf = TypeVar("DQNPolicySelf", bound="DQNPolicy") - - -class DQNPolicy(Policy): - def __init__( - self, - env: VecEnv, - hidden_sizes: Sequence[int] = [], - **kwargs, - ) -> None: - super().__init__(env, **kwargs) - self.q_net = QNetwork(env.observation_space, env.action_space, hidden_sizes) - - def act( - self, obs: VecEnvObs, eps: float = 0, deterministic: bool = True - ) -> np.ndarray: - assert eps == 0 if deterministic else eps >= 0 - if not deterministic and np.random.random() < eps: - return np.array( - [self.env.action_space.sample() for _ in range(self.env.num_envs)] - ) - else: - with torch.no_grad(): - obs_th = torch.as_tensor(np.array(obs)) - if self.device: - obs_th = obs_th.to(self.device) - return self.q_net(obs_th).argmax(axis=1).cpu().numpy() diff --git a/dqn/q_net.py b/dqn/q_net.py deleted file mode 100644 index ca5b563ca2d0ce1a9f46e643ad0eefe41b6b764d..0000000000000000000000000000000000000000 --- a/dqn/q_net.py +++ /dev/null @@ -1,29 +0,0 @@ -import gym -import torch as th -import torch.nn as nn - -from gym.spaces import Discrete -from typing import Sequence, Type - -from shared.module import FeatureExtractor, mlp - - -class QNetwork(nn.Module): - def __init__( - self, - observation_space: gym.Space, - action_space: gym.Space, - hidden_sizes: Sequence[int] = [], - activation: Type[nn.Module] = nn.ReLU, # Used by stable-baselines3 - ) -> None: - super().__init__() - assert isinstance(action_space, Discrete) - self._feature_extractor = FeatureExtractor(observation_space, activation) - layer_sizes = ( - (self._feature_extractor.out_dim,) + tuple(hidden_sizes) + (action_space.n,) - ) - self._fc = mlp(layer_sizes, activation) - - def forward(self, obs: th.Tensor) -> th.Tensor: - x = self._feature_extractor(obs) - return self._fc(x) diff --git a/hyperparams/dqn.yml b/hyperparams/dqn.yml deleted file mode 100644 index f1cb2a34328deb97afd91d68c5e1b14bbefd2990..0000000000000000000000000000000000000000 --- a/hyperparams/dqn.yml +++ /dev/null @@ -1,104 +0,0 @@ -CartPole-v1: &cartpole-defaults - n_timesteps: !!float 5e4 - env_hyperparams: - rolling_length: 50 - policy_hyperparams: - hidden_sizes: [256, 256] - algo_hyperparams: - learning_rate: !!float 2.3e-3 - batch_size: 64 - buffer_size: 100000 - learning_starts: 1000 - gamma: 0.99 - target_update_interval: 10 - train_freq: 256 - gradient_steps: 128 - exploration_fraction: 0.16 - exploration_final_eps: 0.04 - eval_params: - step_freq: !!float 1e4 - -CartPole-v0: - <<: *cartpole-defaults - n_timesteps: !!float 4e4 - -MountainCar-v0: - n_timesteps: !!float 1.2e5 - env_hyperparams: - rolling_length: 50 - policy_hyperparams: - hidden_sizes: [256, 256] - algo_hyperparams: - learning_rate: !!float 4e-3 - batch_size: 128 - buffer_size: 10000 - learning_starts: 1000 - gamma: 0.98 - target_update_interval: 600 - train_freq: 16 - gradient_steps: 8 - exploration_fraction: 0.2 - exploration_final_eps: 0.07 - -Acrobot-v1: - n_timesteps: !!float 1e5 - env_hyperparams: - rolling_length: 50 - policy_hyperparams: - hidden_sizes: [256, 256] - algo_hyperparams: - learning_rate: !!float 6.3e-4 - batch_size: 128 - buffer_size: 50000 - learning_starts: 0 - gamma: 0.99 - target_update_interval: 250 - train_freq: 4 - gradient_steps: -1 - exploration_fraction: 0.12 - exploration_final_eps: 0.1 - -LunarLander-v2: - n_timesteps: !!float 5e5 - env_hyperparams: - rolling_length: 50 - policy_hyperparams: - hidden_sizes: [256, 256] - algo_hyperparams: - learning_rate: !!float 1e-4 - batch_size: 256 - buffer_size: 100000 - learning_starts: 10000 - gamma: 0.99 - target_update_interval: 250 - train_freq: 8 - gradient_steps: -1 - exploration_fraction: 0.12 - exploration_final_eps: 0.1 - max_grad_norm: 0.5 - eval_params: - step_freq: 25_000 - -atari: &atari-defaults - n_timesteps: !!float 1e7 - env_hyperparams: - frame_stack: 4 - no_reward_timeout_steps: 1_000 - n_envs: 8 - vec_env_class: "subproc" - algo_hyperparams: - buffer_size: 100000 - learning_rate: !!float 1e-4 - batch_size: 32 - learning_starts: 100000 - target_update_interval: 1000 - train_freq: 8 - gradient_steps: 2 - exploration_fraction: 0.1 - exploration_final_eps: 0.01 - eval_params: - deterministic: false - -PongNoFrameskip-v4: - <<: *atari-defaults - n_timesteps: !!float 2.5e6 diff --git a/hyperparams/ppo.yml b/hyperparams/ppo.yml deleted file mode 100644 index c77c9f1dae5f3fd4f8241cad15c383e8834b6979..0000000000000000000000000000000000000000 --- a/hyperparams/ppo.yml +++ /dev/null @@ -1,202 +0,0 @@ -CartPole-v1: &cartpole-defaults - n_timesteps: !!float 1e5 - env_hyperparams: - n_envs: 8 - algo_hyperparams: - n_steps: 32 - batch_size: 256 - n_epochs: 20 - gae_lambda: 0.8 - gamma: 0.98 - ent_coef: 0.0 - learning_rate: 0.001 - learning_rate_decay: linear - clip_range: 0.2 - clip_range_decay: linear - eval_params: - step_freq: !!float 2.5e4 - n_episodes: 10 - save_best: true - -CartPole-v0: - <<: *cartpole-defaults - n_timesteps: !!float 5e4 - -MountainCar-v0: - n_timesteps: !!float 1e6 - env_hyperparams: - normalize: true - n_envs: 16 - algo_hyperparams: - n_steps: 16 - n_epochs: 4 - gae_lambda: 0.98 - gamma: 0.99 - ent_coef: 0.0 - -MountainCarContinuous-v0: - n_timesteps: !!float 1e5 - env_hyperparams: - normalize: true - n_envs: 4 - policy_hyperparams: - init_layers_orthogonal: false - # log_std_init: -3.29 - algo_hyperparams: - n_steps: 512 - batch_size: 256 - n_epochs: 10 - learning_rate: !!float 7.77e-5 - ent_coef: 0.01 # 0.00429 - ent_coef_decay: linear - clip_range: 0.1 - gae_lambda: 0.9 - max_grad_norm: 5 - vf_coef: 0.19 - # use_sde: true - eval_params: - step_freq: 5000 - n_episodes: 10 - save_best: true - -Acrobot-v1: - n_timesteps: !!float 1e6 - env_hyperparams: - n_envs: 16 - normalize: true - algo_hyperparams: - n_steps: 256 - n_epochs: 4 - gae_lambda: 0.94 - gamma: 0.99 - ent_coef: 0.0 - -LunarLander-v2: - n_timesteps: !!float 1e6 - env_hyperparams: - n_envs: 16 - algo_hyperparams: - n_steps: 1024 - batch_size: 64 - n_epochs: 4 - gae_lambda: 0.98 - gamma: 0.999 - ent_coef: 0.01 - ent_coef_decay: linear - normalize_advantage: false - eval_params: - step_freq: !!float 5e4 - n_episodes: 10 - save_best: true - -CarRacing-v0: - n_timesteps: !!float 4e6 - env_hyperparams: - n_envs: 8 - frame_stack: 4 - policy_hyperparams: - use_sde: true - log_std_init: -2 - init_layers_orthogonal: false - activation_fn: relu - share_features_extractor: false - cnn_feature_dim: 256 - algo_hyperparams: - n_steps: 512 - batch_size: 128 - n_epochs: 10 - learning_rate: !!float 1e-4 - learning_rate_decay: linear - gamma: 0.99 - gae_lambda: 0.95 - ent_coef: 0.0 - sde_sample_freq: 4 - max_grad_norm: 0.5 - vf_coef: 0.5 - clip_range: 0.2 - -# BreakoutNoFrameskip-v4 -# PongNoFrameskip-v4 -# SpaceInvadersNoFrameskip-v4 -# QbertNoFrameskip-v4 -atari: &atari-defaults - n_timesteps: !!float 1e7 - policy_hyperparams: - activation_fn: relu - env_hyperparams: &atari-env-defaults - n_envs: 8 - frame_stack: 4 - no_reward_timeout_steps: 1000 - no_reward_fire_steps: 500 - vec_env_class: subproc - algo_hyperparams: - n_steps: 128 - batch_size: 256 - n_epochs: 4 - learning_rate: !!float 2.5e-4 - learning_rate_decay: linear - clip_range: 0.1 - clip_range_decay: linear - vf_coef: 0.5 - ent_coef: 0.01 - eval_params: - deterministic: false - -HalfCheetahBulletEnv-v0: &pybullet-defaults - n_timesteps: !!float 2e6 - env_hyperparams: &pybullet-env-defaults - n_envs: 16 - normalize: true - policy_hyperparams: &pybullet-policy-defaults - pi_hidden_sizes: [256, 256] - v_hidden_sizes: [256, 256] - activation_fn: relu - algo_hyperparams: &pybullet-algo-defaults - n_steps: 512 - batch_size: 128 - n_epochs: 20 - gamma: 0.99 - gae_lambda: 0.9 - ent_coef: 0.0 - sde_sample_freq: 4 - max_grad_norm: 0.5 - vf_coef: 0.5 - learning_rate: !!float 3e-5 - clip_range: 0.4 - -AntBulletEnv-v0: - <<: *pybullet-defaults - policy_hyperparams: - <<: *pybullet-policy-defaults - algo_hyperparams: - <<: *pybullet-algo-defaults - -Walker2DBulletEnv-v0: - <<: *pybullet-defaults - algo_hyperparams: - <<: *pybullet-algo-defaults - clip_range_decay: linear - -HopperBulletEnv-v0: - <<: *pybullet-defaults - algo_hyperparams: - <<: *pybullet-algo-defaults - clip_range_decay: linear - -HumanoidBulletEnv-v0: - <<: *pybullet-defaults - n_timesteps: !!float 1e7 - env_hyperparams: - <<: *pybullet-env-defaults - n_envs: 8 - policy_hyperparams: - <<: *pybullet-policy-defaults - # log_std_init: -1 - algo_hyperparams: - <<: *pybullet-algo-defaults - n_steps: 2048 - batch_size: 64 - n_epochs: 10 - gae_lambda: 0.95 - learning_rate: !!float 2.5e-4 - clip_range: 0.2 diff --git a/hyperparams/vpg.yml b/hyperparams/vpg.yml deleted file mode 100644 index c98faf61ed265f4164025a0c8cf0011c8571159b..0000000000000000000000000000000000000000 --- a/hyperparams/vpg.yml +++ /dev/null @@ -1,157 +0,0 @@ -CartPole-v1: &cartpole-defaults - n_timesteps: !!float 4e5 - policy_hyperparams: - hidden_sizes: [32] - algo_hyperparams: - steps_per_epoch: 4096 - pi_lr: 0.01 - gamma: 0.99 - lam: 1 - val_lr: 0.01 - train_v_iters: 80 - eval_params: - step_freq: !!float 2.5e4 - n_episodes: 10 - save_best: true - -CartPole-v0: - <<: *cartpole-defaults - n_timesteps: !!float 1e5 - algo_hyperparams: - steps_per_epoch: 1024 - pi_lr: 0.01 - gamma: 0.99 - lam: 1 - val_lr: 0.01 - train_v_iters: 80 - -Acrobot-v1: - n_timesteps: !!float 2e5 - policy_hyperparams: - hidden_sizes: [32, 32] - algo_hyperparams: - steps_per_epoch: 2048 - pi_lr: 0.005 - gamma: 0.99 - lam: 0.97 - val_lr: 0.01 - train_v_iters: 80 - max_grad_norm: 0.5 - eval_params: - step_freq: !!float 4e4 - n_episodes: 10 - save_best: true - -LunarLander-v2: - n_timesteps: !!float 4e6 - policy_hyperparams: - hidden_sizes: [256, 256] - algo_hyperparams: - steps_per_epoch: 2048 - pi_lr: 0.0001 - gamma: 0.999 - lam: 0.97 - val_lr: 0.0001 - train_v_iters: 80 - max_grad_norm: 0.5 - eval_params: - step_freq: !!float 5e4 - n_episodes: 10 - save_best: true - -CarRacing-v0: - n_timesteps: !!float 4e6 - env_hyperparams: - frame_stack: 4 - n_envs: 4 - vec_env_class: "dummy" - policy_hyperparams: - hidden_sizes: [256, 256] - algo_hyperparams: - steps_per_epoch: 4000 - pi_lr: !!float 7e-5 - gamma: 0.99 - lam: 0.95 - val_lr: !!float 1e-4 - train_v_iters: 40 - max_grad_norm: 0.5 - eval_params: - step_freq: !!float 5e4 - n_episodes: 10 - save_best: true - -HalfCheetahBulletEnv-v0: &pybullet-defaults - n_timesteps: !!float 2e6 - policy_hyperparams: - hidden_sizes: [64, 64] - init_layers_orthogonal: true - algo_hyperparams: - steps_per_epoch: 4000 - pi_lr: !!float 3e-4 - gamma: 0.99 - lam: 0.97 - val_lr: !!float 1e-3 - train_v_iters: 80 - max_grad_norm: 0.5 - eval_params: - step_freq: !!float 1e5 - n_episodes: 10 - save_best: true - -HopperBulletEnv-v0: - <<: *pybullet-defaults - -AntBulletEnv-v0: - <<: *pybullet-defaults - policy_hyperparams: - hidden_sizes: [400, 300] - algo_hyperparams: - pi_lr: !!float 7e-4 - gamma: 0.99 - lam: 0.97 - val_lr: !!float 7e-3 - train_v_iters: 80 - max_grad_norm: 0.5 - -FrozenLake-v1: - n_timesteps: !!float 8e5 - env_params: - make_kwargs: - map_name: 8x8 - is_slippery: true - policy_hyperparams: - hidden_sizes: [64] - algo_hyperparams: - steps_per_epoch: 2048 - pi_lr: 0.01 - gamma: 0.99 - lam: 0.98 - val_lr: 0.01 - train_v_iters: 80 - max_grad_norm: 0.5 - eval_params: - step_freq: !!float 5e4 - n_episodes: 10 - save_best: true - -SpaceInvadersNoFrameskip-v4: &atari-defaults - n_timesteps: !!float 1e7 - env_hyperparams: - frame_stack: 4 - no_reward_timeout_steps: 1_000 - n_envs: 8 - vec_env_class: "subproc" - policy_hyperparams: - hidden_sizes: [256, 256] - algo_hyperparams: - steps_per_epoch: 4096 - pi_lr: !!float 1e-4 - gamma: 0.99 - lam: 0.95 - val_lr: !!float 2e-4 - train_v_iters: 80 - max_grad_norm: 0.5 - eval_params: - step_freq: !!float 1e5 - n_episodes: 10 - save_best: true diff --git a/lambda_labs/benchmark.sh b/lambda_labs/benchmark.sh deleted file mode 100644 index 2b901f8f3fad3c0dafee8098a696f560e89b62f0..0000000000000000000000000000000000000000 --- a/lambda_labs/benchmark.sh +++ /dev/null @@ -1,33 +0,0 @@ -source benchmarks/train_loop.sh - -# export WANDB_PROJECT_NAME="rl-algo-impls" -export VIRTUAL_DISPLAY=1 - -BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-6}" - -ALGOS=( - # "vpg" - "dqn" - # "ppo" -) -ENVS=( - # Basic - "CartPole-v1" - "MountainCar-v0" - # "MountainCarContinuous-v0" - "Acrobot-v1" - "LunarLander-v2" - # # PyBullet - # "HalfCheetahBulletEnv-v0" - # "AntBulletEnv-v0" - # "HopperBulletEnv-v0" - # "Walker2DBulletEnv-v0" - # # CarRacing - # "CarRacing-v0" - # Atari - "PongNoFrameskip-v4" - "BreakoutNoFrameskip-v4" - "SpaceInvadersNoFrameskip-v4" - "QbertNoFrameskip-v4" -) -train_loop "${ALGOS[*]}" "${ENVS[*]}" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD diff --git a/lambda_labs/lambda_requirements.txt b/lambda_labs/lambda_requirements.txt deleted file mode 100644 index 0768efdc491bb11b4f2f797175ebb16d5dfa4f32..0000000000000000000000000000000000000000 --- a/lambda_labs/lambda_requirements.txt +++ /dev/null @@ -1,11 +0,0 @@ -scipy >= 1.10.0, < 1.11 -tensorboard >= ^2.11.0, < 2.12 -AutoROM.accept-rom-license >= 0.4.2, < 0.5 -stable-baselines3[extra] >= 1.7.0, < 1.8 -gym[box2d] >= 0.21.0, < 0.22 -pyglet == 1.5.27 -wandb >= 0.13.9, < 0.14 -pyvirtualdisplay == 3.0 -pybullet >= 3.2.5, < 3.3 -tabulate >= 0.9.0, < 0.10 -huggingface-hub >= 0.12.0, < 0.13 \ No newline at end of file diff --git a/lambda_labs/setup.sh b/lambda_labs/setup.sh deleted file mode 100644 index 79d7f0a0b9bcc087b3b40065830bc8afef29ec18..0000000000000000000000000000000000000000 --- a/lambda_labs/setup.sh +++ /dev/null @@ -1,10 +0,0 @@ -sudo apt update -sudo apt install -y python-opengl -sudo apt install -y ffmpeg -sudo apt install -y xvfb -sudo apt install -y swig - 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pi_hidden_sizes: Optional[Sequence[int]] = None, - v_hidden_sizes: Optional[Sequence[int]] = None, - **kwargs, - ) -> None: - obs_space = env.observation_space - if isinstance(obs_space, Box): - if len(obs_space.shape) == 3: - pi_hidden_sizes = pi_hidden_sizes or [] - v_hidden_sizes = v_hidden_sizes or [] - elif len(obs_space.shape) == 1: - pi_hidden_sizes = pi_hidden_sizes or [64, 64] - v_hidden_sizes = v_hidden_sizes or [64, 64] - else: - raise ValueError(f"Unsupported observation space: {obs_space}") - elif isinstance(obs_space, Discrete): - pi_hidden_sizes = pi_hidden_sizes or [64] - v_hidden_sizes = v_hidden_sizes or [64] - else: - raise ValueError(f"Unsupported observation space: {obs_space}") - super().__init__( - env, - pi_hidden_sizes, - v_hidden_sizes, - **kwargs, - ) diff --git a/ppo/ppo.py b/ppo/ppo.py deleted file mode 100644 index 353d5bae4bb06729bc06d0fa4509e0889dbd8084..0000000000000000000000000000000000000000 --- a/ppo/ppo.py +++ /dev/null @@ -1,367 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn - -from dataclasses import asdict, dataclass -from torch.optim import Adam -from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs -from torch.utils.tensorboard.writer import SummaryWriter -from typing import List, Optional, Sequence, NamedTuple, TypeVar - -from shared.algorithm import Algorithm -from shared.callbacks.callback import Callback -from shared.policy.on_policy import ActorCritic -from shared.schedule import constant_schedule, linear_schedule -from shared.trajectory import Trajectory as BaseTrajectory -from shared.utils import discounted_cumsum - - -@dataclass -class PPOTrajectory(BaseTrajectory): - logp_a: List[float] - next_obs: Optional[np.ndarray] - - def __init__(self) -> None: - super().__init__() - self.logp_a = [] - self.next_obs = None - - def add( - self, - obs: np.ndarray, - act: np.ndarray, - next_obs: np.ndarray, - rew: float, - terminated: bool, - v: float, - logp_a: float, - ): - super().add(obs, act, rew, v) - self.next_obs = next_obs if not terminated else None - self.terminated = terminated - self.logp_a.append(logp_a) - - -class TrajectoryAccumulator: - def __init__(self, num_envs: int) -> None: - self.num_envs = num_envs - - self.trajectories_ = [] - self.current_trajectories_ = [PPOTrajectory() for _ in range(num_envs)] - - def step( - self, - obs: VecEnvObs, - action: np.ndarray, - next_obs: VecEnvObs, - reward: np.ndarray, - done: np.ndarray, - val: np.ndarray, - logp_a: np.ndarray, - ) -> None: - assert isinstance(obs, np.ndarray) - assert isinstance(next_obs, np.ndarray) - for i, trajectory in enumerate(self.current_trajectories_): - # TODO: Eventually take advantage of terminated/truncated differentiation in - # later versions of gym. - trajectory.add( - obs[i], action[i], next_obs[i], reward[i], done[i], val[i], logp_a[i] - ) - if done[i]: - self.trajectories_.append(trajectory) - self.current_trajectories_[i] = PPOTrajectory() - - @property - def all_trajectories(self) -> List[PPOTrajectory]: - return self.trajectories_ + list( - filter(lambda t: len(t), self.current_trajectories_) - ) - - -class RtgAdvantage(NamedTuple): - rewards_to_go: torch.Tensor - advantage: torch.Tensor - - -class TrainStepStats(NamedTuple): - loss: float - pi_loss: float - v_loss: float - entropy_loss: float - approx_kl: float - clipped_frac: float - - -@dataclass -class TrainStats: - loss: float - pi_loss: float - v_loss: float - entropy_loss: float - approx_kl: float - clipped_frac: float - - def __init__(self, step_stats: List[TrainStepStats]) -> None: - self.loss = np.mean([s.loss for s in step_stats]).item() - self.pi_loss = np.mean([s.pi_loss for s in step_stats]).item() - self.v_loss = np.mean([s.v_loss for s in step_stats]).item() - self.entropy_loss = np.mean([s.entropy_loss for s in step_stats]).item() - self.approx_kl = np.mean([s.approx_kl for s in step_stats]).item() - self.clipped_frac = np.mean([s.clipped_frac for s in step_stats]).item() - - def write_to_tensorboard(self, tb_writer: SummaryWriter, global_step: int) -> None: - tb_writer.add_scalars("losses", asdict(self), global_step=global_step) - - def __repr__(self) -> str: - return " | ".join( - [ - f"Loss: {round(self.loss, 2)}", - f"Pi L: {round(self.pi_loss, 2)}", - f"V L: {round(self.v_loss, 2)}", - f"E L: {round(self.entropy_loss, 2)}", - f"Apx KL Div: {round(self.approx_kl, 2)}", - f"Clip Frac: {round(self.clipped_frac, 2)}", - ] - ) - - -PPOSelf = TypeVar("PPOSelf", bound="PPO") - - -class PPO(Algorithm): - def __init__( - self, - policy: ActorCritic, - env: VecEnv, - device: torch.device, - tb_writer: SummaryWriter, - learning_rate: float = 3e-4, - learning_rate_decay: str = "none", - n_steps: int = 2048, - batch_size: int = 64, - n_epochs: int = 10, - gamma: float = 0.99, - gae_lambda: float = 0.95, - clip_range: float = 0.2, - clip_range_decay: str = "none", - clip_range_vf: Optional[float] = None, - clip_range_vf_decay: str = "none", - normalize_advantage: bool = True, - ent_coef: float = 0.0, - ent_coef_decay: str = "none", - vf_coef: float = 0.5, - max_grad_norm: float = 0.5, - update_rtg_between_epochs: bool = False, - sde_sample_freq: int = -1, - ) -> None: - super().__init__(policy, env, device, tb_writer) - self.policy = policy - - self.gamma = gamma - self.gae_lambda = gae_lambda - self.optimizer = Adam(self.policy.parameters(), lr=learning_rate) - self.lr_schedule = ( - linear_schedule(learning_rate, 0) - if learning_rate_decay == "linear" - else constant_schedule(learning_rate) - ) - self.max_grad_norm = max_grad_norm - self.clip_range_schedule = ( - linear_schedule(clip_range, 0) - if clip_range_decay == "linear" - else constant_schedule(clip_range) - ) - self.clip_range_vf_schedule = None - if clip_range_vf: - self.clip_range_vf_schedule = ( - linear_schedule(clip_range_vf, 0) - if clip_range_vf_decay == "linear" - else constant_schedule(clip_range_vf) - ) - self.normalize_advantage = normalize_advantage - self.ent_coef_schedule = ( - linear_schedule(ent_coef, 0) - if ent_coef_decay == "linear" - else constant_schedule(ent_coef) - ) - self.vf_coef = vf_coef - - self.n_steps = n_steps - self.batch_size = batch_size - self.n_epochs = n_epochs - self.sde_sample_freq = sde_sample_freq - - self.update_rtg_between_epochs = update_rtg_between_epochs - - def learn( - self: PPOSelf, - total_timesteps: int, - callback: Optional[Callback] = None, - ) -> PPOSelf: - obs = self.env.reset() - ts_elapsed = 0 - while ts_elapsed < total_timesteps: - accumulator = self._collect_trajectories(obs) - progress = ts_elapsed / total_timesteps - train_stats = self.train(accumulator.all_trajectories, progress) - rollout_steps = self.n_steps * self.env.num_envs - ts_elapsed += rollout_steps - train_stats.write_to_tensorboard(self.tb_writer, ts_elapsed) - if callback: - callback.on_step(timesteps_elapsed=rollout_steps) - - return self - - def _collect_trajectories(self, obs: VecEnvObs) -> TrajectoryAccumulator: - self.policy.eval() - accumulator = TrajectoryAccumulator(self.env.num_envs) - self.policy.reset_noise() - for i in range(self.n_steps): - if self.sde_sample_freq > 0 and i > 0 and i % self.sde_sample_freq == 0: - self.policy.reset_noise() - action, value, logp_a, clamped_action = self.policy.step(obs) - next_obs, reward, done, _ = self.env.step(clamped_action) - accumulator.step(obs, action, next_obs, reward, done, value, logp_a) - obs = next_obs - return accumulator - - def train(self, trajectories: List[PPOTrajectory], progress: float) -> TrainStats: - self.policy.train() - learning_rate = self.lr_schedule(progress) - self.optimizer.param_groups[0]["lr"] = learning_rate - - pi_clip = self.clip_range_schedule(progress) - v_clip = ( - self.clip_range_vf_schedule(progress) - if self.clip_range_vf_schedule - else None - ) - ent_coef = self.ent_coef_schedule(progress) - - obs = torch.as_tensor( - np.concatenate([np.array(t.obs) for t in trajectories]), device=self.device - ) - act = torch.as_tensor( - np.concatenate([np.array(t.act) for t in trajectories]), device=self.device - ) - rtg, adv = self._compute_rtg_and_advantage(trajectories) - orig_v = torch.as_tensor( - np.concatenate([np.array(t.v) for t in trajectories]), device=self.device - ) - orig_logp_a = torch.as_tensor( - np.concatenate([np.array(t.logp_a) for t in trajectories]), - device=self.device, - ) - - step_stats = [] - for _ in range(self.n_epochs): - if self.update_rtg_between_epochs: - rtg, adv = self._compute_rtg_and_advantage(trajectories) - else: - adv = self._compute_advantage(trajectories) - idxs = torch.randperm(len(obs)) - for i in range(0, len(obs), self.batch_size): - mb_idxs = idxs[i : i + self.batch_size] - mb_adv = adv[mb_idxs] - if self.normalize_advantage: - mb_adv = (mb_adv - mb_adv.mean(-1)) / (mb_adv.std(-1) + 1e-8) - step_stats.append( - self._train_step( - pi_clip, - v_clip, - ent_coef, - obs[mb_idxs], - act[mb_idxs], - rtg[mb_idxs], - mb_adv, - orig_v[mb_idxs], - orig_logp_a[mb_idxs], - ) - ) - - return TrainStats(step_stats) - - def _train_step( - self, - pi_clip: float, - v_clip: Optional[float], - ent_coef: float, - obs: torch.Tensor, - act: torch.Tensor, - rtg: torch.Tensor, - adv: torch.Tensor, - orig_v: torch.Tensor, - orig_logp_a: torch.Tensor, - ) -> TrainStepStats: - logp_a, entropy, v = self.policy(obs, act) - logratio = logp_a - orig_logp_a - ratio = torch.exp(logratio) - clip_ratio = torch.clamp(ratio, min=1 - pi_clip, max=1 + pi_clip) - pi_loss = torch.maximum(-ratio * adv, -clip_ratio * adv).mean() - - v_loss = (v - rtg).pow(2) - if v_clip: - v_clipped = (torch.clamp(v, orig_v - v_clip, orig_v + v_clip) - rtg).pow(2) - v_loss = torch.maximum(v_loss, v_clipped) - v_loss = v_loss.mean() - - entropy_loss = entropy.mean() - - loss = pi_loss - ent_coef * entropy_loss + self.vf_coef * v_loss - - self.optimizer.zero_grad() - loss.backward() - nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm) - self.optimizer.step() - - with torch.no_grad(): - approx_kl = ((ratio - 1) - logratio).mean().cpu().numpy().item() - clipped_frac = ( - ((ratio - 1).abs() > pi_clip).float().mean().cpu().numpy().item() - ) - return TrainStepStats( - loss.item(), - pi_loss.item(), - v_loss.item(), - entropy_loss.item(), - approx_kl, - clipped_frac, - ) - - def _compute_advantage(self, trajectories: Sequence[PPOTrajectory]) -> torch.Tensor: - advantage = [] - for traj in trajectories: - last_val = 0 - if not traj.terminated and traj.next_obs is not None: - last_val = self.policy.value(np.array(traj.next_obs)) - rew = np.append(np.array(traj.rew), last_val) - v = np.append(np.array(traj.v), last_val) - deltas = rew[:-1] + self.gamma * v[1:] - v[:-1] - advantage.append(discounted_cumsum(deltas, self.gamma * self.gae_lambda)) - return torch.as_tensor( - np.concatenate(advantage), dtype=torch.float32, device=self.device - ) - - def _compute_rtg_and_advantage( - self, trajectories: Sequence[PPOTrajectory] - ) -> RtgAdvantage: - rewards_to_go = [] - advantages = [] - for traj in trajectories: - last_val = 0 - if not traj.terminated and traj.next_obs is not None: - last_val = self.policy.value(np.array(traj.next_obs)) - rew = np.append(np.array(traj.rew), last_val) - v = np.append(np.array(traj.v), last_val) - deltas = rew[:-1] + self.gamma * v[1:] - v[:-1] - adv = discounted_cumsum(deltas, self.gamma * self.gae_lambda) - advantages.append(adv) - rewards_to_go.append(v[:-1] + adv) - return RtgAdvantage( - torch.as_tensor( - np.concatenate(rewards_to_go), dtype=torch.float32, device=self.device - ), - torch.as_tensor( - np.concatenate(advantages), dtype=torch.float32, device=self.device - ), - ) diff --git a/publish/markdown_format.py b/publish/markdown_format.py deleted file mode 100644 index 1e48fd1ef85ce19ad952b04433911b3fe11bf395..0000000000000000000000000000000000000000 --- a/publish/markdown_format.py +++ /dev/null @@ -1,210 +0,0 @@ -import os -import pandas as pd -import wandb.apis.public -import yaml - -from collections import defaultdict -from dataclasses import dataclass, asdict -from typing import Any, Dict, Iterable, List, NamedTuple, Optional, TypeVar -from urllib.parse import urlparse - -from runner.evaluate import Evaluation - -EvaluationRowSelf = TypeVar("EvaluationRowSelf", bound="EvaluationRow") - - -@dataclass -class EvaluationRow: - algo: str - env: str - seed: Optional[int] - reward_mean: float - reward_std: float - eval_episodes: int - best: str - wandb_url: str - - @staticmethod - def data_frame(rows: List[EvaluationRowSelf]) -> pd.DataFrame: - results = defaultdict(list) - for r in rows: - for k, v in asdict(r).items(): - results[k].append(v) - return pd.DataFrame(results) - - -class EvalTableData(NamedTuple): - run: wandb.apis.public.Run - evaluation: Evaluation - - -def evaluation_table(table_data: Iterable[EvalTableData]) -> str: - best_stats = sorted( - [d.evaluation.stats for d in table_data], key=lambda r: r.score, reverse=True - )[0] - table_data = sorted(table_data, key=lambda d: d.evaluation.config.seed() or 0) - rows = [ - EvaluationRow( - config.algo, - config.env_id, - config.seed(), - stats.score.mean, - stats.score.std, - len(stats), - "*" if stats == best_stats else "", - f"[wandb]({r.url})", - ) - for (r, (_, stats, config)) in table_data - ] - df = EvaluationRow.data_frame(rows) - return df.to_markdown(index=False) - - -def github_project_link(github_url: str) -> str: - return f"[{urlparse(github_url).path}]({github_url})" - - -def header_section(algo: str, env: str, github_url: str, wandb_report_url: str) -> str: - algo_caps = algo.upper() - lines = [ - f"# **{algo_caps}** Agent playing **{env}**", - f"This is a trained model of a **{algo_caps}** agent playing **{env}** using " - f"the {github_project_link(github_url)} repo.", - f"All models trained at this commit can be found at {wandb_report_url}.", - ] - return "\n\n".join(lines) - - -def github_tree_link(github_url: str, commit_hash: Optional[str]) -> str: - if not commit_hash: - return github_project_link(github_url) - return f"[{commit_hash[:7]}]({github_url}/tree/{commit_hash})" - - -def results_section( - table_data: List[EvalTableData], algo: str, github_url: str, commit_hash: str -) -> str: - # type: ignore - lines = [ - "## Training Results", - f"This model was trained from {len(table_data)} trainings of **{algo.upper()}** " - + "agents using different initial seeds. " - + f"These agents were trained by checking out " - + f"{github_tree_link(github_url, commit_hash)}. " - + "The best and last models were kept from each training. " - + "This submission has loaded the best models from each training, reevaluates " - + "them, and selects the best model from these latest evaluations (mean - std).", - ] - lines.append(evaluation_table(table_data)) - return "\n\n".join(lines) - - -def prerequisites_section() -> str: - return """ -### Prerequisites: Weights & Biases (WandB) -Training and benchmarking assumes you have a Weights & Biases project to upload runs to. -By default training goes to a rl-algo-impls project while benchmarks go to -rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best -models and the model weights are uploaded to WandB. - -Before doing anything below, you'll need to create a wandb account and run `wandb -login`. -""" - - -def usage_section(github_url: str, run_path: str, commit_hash: str) -> str: - return f""" -## Usage -{urlparse(github_url).path}: {github_url} - -Note: While the model state dictionary and hyperaparameters are saved, the latest -implementation could be sufficiently different to not be able to reproduce similar -results. You might need to checkout the commit the agent was trained on: -{github_tree_link(github_url, commit_hash)}. -``` -# Downloads the model, sets hyperparameters, and runs agent for 3 episodes -python enjoy.py --wandb-run-path={run_path} -``` - -Setup hasn't been completely worked out yet, so you might be best served by using Google -Colab starting from the -[colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb) -notebook. -""" - - -def training_setion( - github_url: str, commit_hash: str, algo: str, env: str, seed: Optional[int] -) -> str: - return f""" -## Training -If you want the highest chance to reproduce these results, you'll want to checkout the -commit the agent was trained on: {github_tree_link(github_url, commit_hash)}. While -training is deterministic, different hardware will give different results. - -``` -python train.py --algo {algo} --env {env} {'--seed ' + str(seed) if seed is not None else ''} -``` - -Setup hasn't been completely worked out yet, so you might be best served by using Google -Colab starting from the -[colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb) -notebook. -""" - - -def benchmarking_section(report_url: str) -> str: - return f""" -## Benchmarking (with Lambda Labs instance) -This and other models from {report_url} were generated by running a script on a Lambda -Labs instance. In a Lambda Labs instance terminal: -``` -git clone git@github.com:sgoodfriend/rl-algo-impls.git -cd rl-algo-impls -bash ./lambda_labs/setup.sh -wandb login -bash ./lambda_labs/benchmark.sh -``` - -### Alternative: Google Colab Pro+ -As an alternative, -[colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb), -can be used. However, this requires a Google Colab Pro+ subscription and running across -4 separate instances because otherwise running all jobs will exceed the 24-hour limit. -""" - - -def hyperparams_section(run_config: Dict[str, Any]) -> str: - return f""" -## Hyperparameters -This isn't exactly the format of hyperparams in {os.path.join("hyperparams", -run_config["algo"] + ".yml")}, but instead the Wandb Run Config. However, it's very -close and has some additional data: -``` -{yaml.dump(run_config)} -``` -""" - - -def model_card_text( - algo: str, - env: str, - github_url: str, - commit_hash: str, - wandb_report_url: str, - table_data: List[EvalTableData], - best_eval: EvalTableData, -) -> str: - run, (_, _, config) = best_eval - run_path = "/".join(run.path) - return "\n\n".join( - [ - header_section(algo, env, github_url, wandb_report_url), - results_section(table_data, algo, github_url, commit_hash), - prerequisites_section(), - usage_section(github_url, run_path, commit_hash), - training_setion(github_url, commit_hash, algo, env, config.seed()), - benchmarking_section(wandb_report_url), - hyperparams_section(run.config), - ] - ) diff --git a/replay.meta.json b/replay.meta.json index 311e0a72bcfc2720b5f3eaecbb3173ba0aeaaa47..0ffa0ef914f7627d0f861e4b4586d14dc1aba0e8 100644 --- a/replay.meta.json +++ b/replay.meta.json @@ -1 +1 @@ -{"content_type": "video/mp4", "encoder_version": {"backend": "ffmpeg", "version": "b'ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers\\nbuilt with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)\\nconfiguration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\\nlibavutil 56. 31.100 / 56. 31.100\\nlibavcodec 58. 54.100 / 58. 54.100\\nlibavformat 58. 29.100 / 58. 29.100\\nlibavdevice 58. 8.100 / 58. 8.100\\nlibavfilter 7. 57.100 / 7. 57.100\\nlibavresample 4. 0. 0 / 4. 0. 0\\nlibswscale 5. 5.100 / 5. 5.100\\nlibswresample 3. 5.100 / 3. 5.100\\nlibpostproc 55. 5.100 / 55. 5.100\\n'", "cmdline": ["ffmpeg", "-nostats", "-loglevel", "error", "-y", "-f", "rawvideo", "-s:v", "600x400", "-pix_fmt", "rgb24", "-framerate", "30", "-i", "-", "-vf", "scale=trunc(iw/2)*2:trunc(ih/2)*2", "-vcodec", "libx264", "-pix_fmt", "yuv420p", "-r", "30", "/tmp/tmppm8jr0d0/dqn-MountainCar-v0/replay.mp4"]}, "episode": {"r": -91.0, "l": 91, "t": 1.597634}} \ No newline at end of file +{"content_type": "video/mp4", "encoder_version": {"backend": "ffmpeg", "version": "b'ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers\\nbuilt with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)\\nconfiguration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\\nlibavutil 56. 31.100 / 56. 31.100\\nlibavcodec 58. 54.100 / 58. 54.100\\nlibavformat 58. 29.100 / 58. 29.100\\nlibavdevice 58. 8.100 / 58. 8.100\\nlibavfilter 7. 57.100 / 7. 57.100\\nlibavresample 4. 0. 0 / 4. 0. 0\\nlibswscale 5. 5.100 / 5. 5.100\\nlibswresample 3. 5.100 / 3. 5.100\\nlibpostproc 55. 5.100 / 55. 5.100\\n'", "cmdline": ["ffmpeg", "-nostats", "-loglevel", "error", "-y", "-f", "rawvideo", "-s:v", "600x400", "-pix_fmt", "rgb24", "-framerate", "30", "-i", "-", "-vf", "scale=trunc(iw/2)*2:trunc(ih/2)*2", "-vcodec", "libx264", "-pix_fmt", "yuv420p", "-r", "30", "/tmp/tmp5m_9l9cv/dqn-MountainCar-v0/replay.mp4"]}, "episode": {"r": -91.0, "l": 91, "t": 1.231273}} \ No newline at end of file diff --git a/rl_algo_impls/benchmark_publish.py b/rl_algo_impls/benchmark_publish.py index 1d2010efab9a48121f4b79cc7192ac6a7e5524b0..2e1045f01c316068f9192db791ae6cc10b617df6 100644 --- a/rl_algo_impls/benchmark_publish.py +++ b/rl_algo_impls/benchmark_publish.py @@ -54,8 +54,8 @@ def benchmark_publish() -> None: "--virtual-display", action="store_true", help="Use headless virtual display" ) # parser.set_defaults( - # wandb_tags=["benchmark_e47a44c", "host_129-146-2-230"], - # wandb_report_url="https://api.wandb.ai/links/sgoodfriend/v4wd7cp5", + # wandb_tags=["benchmark_2067e21", "host_155-248-199-228"], + # wandb_report_url="https://api.wandb.ai/links/sgoodfriend/09frjfcs", # envs=[], # exclude_envs=[], # ) diff --git a/rl_algo_impls/huggingface_publish.py b/rl_algo_impls/huggingface_publish.py index 5cf482357b0c44703a30f88b2e70a92542153781..afe1d57345bfc831044f65f331f1031310fc3dfe 100644 --- a/rl_algo_impls/huggingface_publish.py +++ b/rl_algo_impls/huggingface_publish.py @@ -162,6 +162,7 @@ def publish( path_in_repo="", commit_message=f"{algo.upper()} playing {env_id} from {github_url}/tree/{commit_hash}", token=huggingface_token, + delete_patterns="*", ) print(f"Pushed model to the hub: {repo_url}") diff --git a/runner/config.py b/runner/config.py deleted file mode 100644 index 0900fcd498e3e277ab16af416b5b25c3036da0d8..0000000000000000000000000000000000000000 --- a/runner/config.py +++ /dev/null @@ -1,133 +0,0 @@ -import os - -from datetime import datetime -from dataclasses import dataclass -from typing import Any, Dict, Optional, TypedDict, Union - - -@dataclass -class RunArgs: - algo: str - env: str - seed: Optional[int] = None - use_deterministic_algorithms: bool = True - - -class Hyperparams(TypedDict, total=False): - device: str - n_timesteps: Union[int, float] - env_hyperparams: Dict[str, Any] - policy_hyperparams: Dict[str, Any] - algo_hyperparams: Dict[str, Any] - eval_params: Dict[str, Any] - - -@dataclass -class Config: - args: RunArgs - hyperparams: Hyperparams - root_dir: str - run_id: str = datetime.now().isoformat() - - def seed(self, training: bool = True) -> Optional[int]: - seed = self.args.seed - if training or seed is None: - return seed - return seed + self.env_hyperparams.get("n_envs", 1) - - @property - def device(self) -> str: - return self.hyperparams.get("device", "auto") - - @property - def n_timesteps(self) -> int: - return int(self.hyperparams.get("n_timesteps", 100_000)) - - @property - def env_hyperparams(self) -> Dict[str, Any]: - return self.hyperparams.get("env_hyperparams", {}) - - @property - def policy_hyperparams(self) -> Dict[str, Any]: - return self.hyperparams.get("policy_hyperparams", {}) - - @property - def algo_hyperparams(self) -> Dict[str, Any]: - return self.hyperparams.get("algo_hyperparams", {}) - - @property - def eval_params(self) -> Dict[str, Any]: - return self.hyperparams.get("eval_params", {}) - - @property - def algo(self) -> str: - return self.args.algo - - @property - def env_id(self) -> str: - return self.args.env - - def model_name(self, include_seed: bool = True) -> str: - parts = [self.algo, self.env_id] - if include_seed and self.args.seed is not None: - parts.append(f"S{self.args.seed}") - make_kwargs = self.env_hyperparams.get("make_kwargs", {}) - if make_kwargs: - for k, v in make_kwargs.items(): - if type(v) == bool and v: - parts.append(k) - elif type(v) == int and v: - parts.append(f"{k}{v}") - else: - parts.append(str(v)) - return "-".join(parts) - - @property - def run_name(self) -> str: - parts = [self.model_name(), self.run_id] - return "-".join(parts) - - @property - def saved_models_dir(self) -> str: - return os.path.join(self.root_dir, "saved_models") - - @property - def downloaded_models_dir(self) -> str: - return os.path.join(self.root_dir, "downloaded_models") - - def model_dir_name( - self, - best: bool = False, - extension: str = "", - ) -> str: - return self.model_name() + ("-best" if best else "") + extension - - def model_dir_path(self, best: bool = False, downloaded: bool = False) -> str: - return os.path.join( - self.saved_models_dir if not downloaded else self.downloaded_models_dir, - self.model_dir_name(best=best), - ) - - @property - def runs_dir(self) -> str: - return os.path.join(self.root_dir, "runs") - - @property - def tensorboard_summary_path(self) -> str: - return os.path.join(self.runs_dir, self.run_name) - - @property - def logs_path(self) -> str: - return os.path.join(self.runs_dir, f"log.yml") - - @property - def videos_dir(self) -> str: - return os.path.join(self.root_dir, "videos") - - @property - def video_prefix(self) -> str: - return os.path.join(self.videos_dir, self.model_name()) - - @property - def best_videos_dir(self) -> str: - return os.path.join(self.videos_dir, f"{self.model_name()}-best") diff --git a/runner/env.py b/runner/env.py deleted file mode 100644 index 7e30998efd71ce50e250dd7ccb4e92da5bf1e152..0000000000000000000000000000000000000000 --- a/runner/env.py +++ /dev/null @@ -1,134 +0,0 @@ -import gym -import os - -from gym.wrappers.resize_observation import ResizeObservation -from gym.wrappers.gray_scale_observation import GrayScaleObservation -from gym.wrappers.frame_stack import FrameStack -from stable_baselines3.common.atari_wrappers import ( - MaxAndSkipEnv, - NoopResetEnv, -) -from stable_baselines3.common.vec_env.base_vec_env import VecEnv -from stable_baselines3.common.vec_env.dummy_vec_env import DummyVecEnv -from stable_baselines3.common.vec_env.subproc_vec_env import SubprocVecEnv -from stable_baselines3.common.vec_env.vec_normalize import VecNormalize -from torch.utils.tensorboard.writer import SummaryWriter -from typing import Any, Callable, Dict, Optional, Union - -from runner.config import Config -from shared.policy.policy import VEC_NORMALIZE_FILENAME -from wrappers.atari_wrappers import EpisodicLifeEnv, FireOnLifeStarttEnv, ClipRewardEnv -from wrappers.episode_record_video import EpisodeRecordVideo -from wrappers.episode_stats_writer import EpisodeStatsWriter -from wrappers.initial_step_truncate_wrapper import InitialStepTruncateWrapper -from wrappers.video_compat_wrapper import VideoCompatWrapper - - -def make_env( - config: Config, - training: bool = True, - render: bool = False, - normalize_load_path: Optional[str] = None, - n_envs: int = 1, - frame_stack: int = 1, - make_kwargs: Optional[Dict[str, Any]] = None, - no_reward_timeout_steps: Optional[int] = None, - no_reward_fire_steps: Optional[int] = None, - vec_env_class: str = "dummy", - normalize: bool = False, - normalize_kwargs: Optional[Dict[str, Any]] = None, - tb_writer: Optional[SummaryWriter] = None, - rolling_length: int = 100, - train_record_video: bool = False, - video_step_interval: Union[int, float] = 1_000_000, - initial_steps_to_truncate: Optional[int] = None, -) -> VecEnv: - if "BulletEnv" in config.env_id: - import pybullet_envs - - make_kwargs = make_kwargs if make_kwargs is not None else {} - if "BulletEnv" in config.env_id and render: - make_kwargs["render"] = True - if "CarRacing" in config.env_id: - make_kwargs["verbose"] = 0 - - spec = gym.spec(config.env_id) - - def make(idx: int) -> Callable[[], gym.Env]: - def _make() -> gym.Env: - env = gym.make(config.env_id, **make_kwargs) - env = gym.wrappers.RecordEpisodeStatistics(env) - env = VideoCompatWrapper(env) - if training and train_record_video and idx == 0: - env = EpisodeRecordVideo( - env, - config.video_prefix, - step_increment=n_envs, - video_step_interval=int(video_step_interval), - ) - if training and initial_steps_to_truncate: - env = InitialStepTruncateWrapper( - env, idx * initial_steps_to_truncate // n_envs - ) - if "AtariEnv" in spec.entry_point: # type: ignore - env = NoopResetEnv(env, noop_max=30) - env = MaxAndSkipEnv(env, skip=4) - env = EpisodicLifeEnv(env, training=training) - action_meanings = env.unwrapped.get_action_meanings() - if "FIRE" in action_meanings: # type: ignore - env = FireOnLifeStarttEnv(env, action_meanings.index("FIRE")) - env = ClipRewardEnv(env, training=training) - env = ResizeObservation(env, (84, 84)) - env = GrayScaleObservation(env, keep_dim=False) - env = FrameStack(env, frame_stack) - elif "CarRacing" in config.env_id: - env = ResizeObservation(env, (64, 64)) - env = GrayScaleObservation(env, keep_dim=False) - env = FrameStack(env, frame_stack) - - if no_reward_timeout_steps: - from wrappers.no_reward_timeout import NoRewardTimeout - - env = NoRewardTimeout( - env, no_reward_timeout_steps, n_fire_steps=no_reward_fire_steps - ) - - seed = config.seed(training=training) - if seed is not None: - env.seed(seed + idx) - env.action_space.seed(seed + idx) - env.observation_space.seed(seed + idx) - - return env - - return _make - - VecEnvClass = {"dummy": DummyVecEnv, "subproc": SubprocVecEnv}[vec_env_class] - venv = VecEnvClass([make(i) for i in range(n_envs)]) - if training: - assert tb_writer - venv = EpisodeStatsWriter( - venv, tb_writer, training=training, rolling_length=rolling_length - ) - if normalize: - if normalize_load_path: - venv = VecNormalize.load( - os.path.join(normalize_load_path, VEC_NORMALIZE_FILENAME), venv - ) - else: - venv = VecNormalize(venv, training=training, **(normalize_kwargs or {})) - if not training: - venv.norm_reward = False - return venv - - -def make_eval_env( - config: Config, override_n_envs: Optional[int] = None, **kwargs -) -> VecEnv: - kwargs = kwargs.copy() - kwargs["training"] = False - if override_n_envs is not None: - kwargs["n_envs"] = override_n_envs - if override_n_envs == 1: - kwargs["vec_env_class"] = "dummy" - return make_env(config, **kwargs) diff --git a/runner/evaluate.py b/runner/evaluate.py deleted file mode 100644 index 2675597169655fb41ef4b60ac8247bdc82c9c21f..0000000000000000000000000000000000000000 --- a/runner/evaluate.py +++ /dev/null @@ -1,103 +0,0 @@ -import os -import shutil - -from dataclasses import dataclass -from typing import NamedTuple, Optional - -from runner.env import make_eval_env -from runner.config import Config, RunArgs -from runner.running_utils import ( - load_hyperparams, - set_seeds, - get_device, - make_policy, -) -from shared.callbacks.eval_callback import evaluate -from shared.policy.policy import Policy -from shared.stats import EpisodesStats - - -@dataclass -class EvalArgs(RunArgs): - render: bool = True - best: bool = True - n_envs: Optional[int] = 1 - n_episodes: int = 3 - deterministic_eval: Optional[bool] = None - no_print_returns: bool = False - wandb_run_path: Optional[str] = None - - -class Evaluation(NamedTuple): - policy: Policy - stats: EpisodesStats - config: Config - - -def evaluate_model(args: EvalArgs, root_dir: str) -> Evaluation: - if args.wandb_run_path: - import wandb - - api = wandb.Api() - run = api.run(args.wandb_run_path) - hyperparams = run.config - - args.algo = hyperparams["algo"] - args.env = hyperparams["env"] - args.seed = hyperparams.get("seed", None) - args.use_deterministic_algorithms = hyperparams.get( - "use_deterministic_algorithms", True - ) - - config = Config(args, hyperparams, root_dir) - model_path = config.model_dir_path(best=args.best, downloaded=True) - - model_archive_name = config.model_dir_name(best=args.best, extension=".zip") - run.file(model_archive_name).download() - if os.path.isdir(model_path): - shutil.rmtree(model_path) - shutil.unpack_archive(model_archive_name, model_path) - os.remove(model_archive_name) - else: - hyperparams = load_hyperparams(args.algo, args.env, root_dir) - - config = Config(args, hyperparams, root_dir) - model_path = config.model_dir_path(best=args.best) - - print(args) - - set_seeds(args.seed, args.use_deterministic_algorithms) - - env = make_eval_env( - config, - override_n_envs=args.n_envs, - render=args.render, - normalize_load_path=model_path, - **config.env_hyperparams, - ) - device = get_device(config.device, env) - policy = make_policy( - args.algo, - env, - device, - load_path=model_path, - **config.policy_hyperparams, - ).eval() - - deterministic = ( - args.deterministic_eval - if args.deterministic_eval is not None - else config.eval_params.get("deterministic", True) - ) - return Evaluation( - policy, - evaluate( - env, - policy, - args.n_episodes, - render=args.render, - deterministic=deterministic, - print_returns=not args.no_print_returns, - ), - config, - ) diff --git a/runner/running_utils.py b/runner/running_utils.py deleted file mode 100644 index aaf0e37c5b49ba87d462ad465cc0b140f067643d..0000000000000000000000000000000000000000 --- a/runner/running_utils.py +++ /dev/null @@ -1,188 +0,0 @@ -import argparse -import gym -import json -import matplotlib.pyplot as plt -import numpy as np -import os -import random -import torch -import torch.backends.cudnn -import yaml - -from gym.spaces import Box, Discrete -from stable_baselines3.common.vec_env.base_vec_env import VecEnv -from torch.utils.tensorboard.writer import SummaryWriter -from typing import Dict, Optional, Type, Union - -from runner.config import Hyperparams -from shared.algorithm import Algorithm -from shared.callbacks.eval_callback import EvalCallback -from shared.policy.policy import Policy - -from dqn.dqn import DQN -from dqn.policy import DQNPolicy -from vpg.vpg import VanillaPolicyGradient -from vpg.policy import VPGActorCritic -from ppo.ppo import PPO -from ppo.policy import PPOActorCritic - -ALGOS: Dict[str, Type[Algorithm]] = { - "dqn": DQN, - "vpg": VanillaPolicyGradient, - "ppo": PPO, -} -POLICIES: Dict[str, Type[Policy]] = { - "dqn": DQNPolicy, - "vpg": VPGActorCritic, - "ppo": PPOActorCritic, -} - -HYPERPARAMS_PATH = "hyperparams" - - -def base_parser(multiple: bool = True) -> argparse.ArgumentParser: - parser = argparse.ArgumentParser() - parser.add_argument( - "--algo", - default=["dqn"], - type=str, - choices=list(ALGOS.keys()), - nargs="+" if multiple else 1, - help="Abbreviation(s) of algorithm(s)", - ) - parser.add_argument( - "--env", - default=["CartPole-v1"], - type=str, - nargs="+" if multiple else 1, - help="Name of environment(s) in gym", - ) - parser.add_argument( - "--seed", - default=[1], - type=int, - nargs="*" if multiple else "?", - help="Seeds to run experiment. Unset will do one run with no set seed", - ) - parser.add_argument( - "--use-deterministic-algorithms", - default=True, - type=bool, - help="If seed set, set torch.use_deterministic_algorithms", - ) - return parser - - -def load_hyperparams(algo: str, env_id: str, root_path: str) -> Hyperparams: - hyperparams_path = os.path.join(root_path, HYPERPARAMS_PATH, f"{algo}.yml") - with open(hyperparams_path, "r") as f: - hyperparams_dict = yaml.safe_load(f) - if "BulletEnv" in env_id: - import pybullet_envs - spec = gym.spec(env_id) - if env_id in hyperparams_dict: - return hyperparams_dict[env_id] - elif "AtariEnv" in str(spec.entry_point) and "atari" in hyperparams_dict: - return hyperparams_dict["atari"] - else: - raise ValueError(f"{env_id} not specified in {algo} hyperparameters file") - - -def get_device(device: str, env: VecEnv) -> torch.device: - # cuda by default - if device == "auto": - device = "cuda" - # Apple MPS is a second choice (sometimes) - if device == "cuda" and not torch.cuda.is_available(): - device = "mps" - # If no MPS, fallback to cpu - if device == "mps" and not torch.backends.mps.is_available(): - device = "cpu" - # Simple environments like Discreet and 1-D Boxes might also be better - # served with the CPU. - if device == "mps": - obs_space = env.observation_space - if isinstance(obs_space, Discrete): - device = "cpu" - elif isinstance(obs_space, Box) and len(obs_space.shape) == 1: - device = "cpu" - print(f"Device: {device}") - return torch.device(device) - - -def set_seeds(seed: Optional[int], use_deterministic_algorithms: bool) -> None: - if seed is None: - return - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.backends.cudnn.benchmark = False - torch.use_deterministic_algorithms(use_deterministic_algorithms) - os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" - - -def make_policy( - algo: str, - env: VecEnv, - device: torch.device, - load_path: Optional[str] = None, - **kwargs, -) -> Policy: - policy = POLICIES[algo](env, **kwargs).to(device) - if load_path: - policy.load(load_path) - return policy - - -def plot_eval_callback(callback: EvalCallback, tb_writer: SummaryWriter, run_name: str): - figure = plt.figure() - cumulative_steps = [ - (idx + 1) * callback.step_freq for idx in range(len(callback.stats)) - ] - plt.plot( - cumulative_steps, - [s.score.mean for s in callback.stats], - "b-", - label="mean", - ) - plt.plot( - cumulative_steps, - [s.score.mean - s.score.std for s in callback.stats], - "g--", - label="mean-std", - ) - plt.fill_between( - cumulative_steps, - [s.score.min for s in callback.stats], # type: ignore - [s.score.max for s in callback.stats], # type: ignore - facecolor="cyan", - label="range", - ) - plt.xlabel("Steps") - plt.ylabel("Score") - plt.legend() - plt.title(f"Eval {run_name}") - tb_writer.add_figure("eval", figure) - - -Scalar = Union[bool, str, float, int, None] - - -def flatten_hyperparameters( - hyperparams: Hyperparams, args: Dict[str, Union[Scalar, list]] -) -> Dict[str, Scalar]: - flattened = args.copy() - for k, v in flattened.items(): - if isinstance(v, list): - flattened[k] = json.dumps(v) - for k, v in hyperparams.items(): - if isinstance(v, dict): - for sk, sv in v.items(): - key = f"{k}/{sk}" - if isinstance(sv, dict) or isinstance(sv, list): - flattened[key] = str(sv) - else: - flattened[key] = sv - else: - flattened[k] = v # type: ignore - return flattened # type: ignore diff --git a/runner/train.py b/runner/train.py deleted file mode 100644 index 8fbb4bd5001cebf737d3d2d355742952b5eac494..0000000000000000000000000000000000000000 --- a/runner/train.py +++ /dev/null @@ -1,126 +0,0 @@ -# Support for PyTorch mps mode (https://pytorch.org/docs/stable/notes/mps.html) -import os - -os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" - -import dataclasses -import shutil -import wandb -import yaml - -from dataclasses import dataclass -from torch.utils.tensorboard.writer import SummaryWriter -from typing import Any, Dict, Optional, Sequence - -from shared.callbacks.eval_callback import EvalCallback -from runner.env import make_env, make_eval_env -from runner.config import Config, RunArgs -from runner.running_utils import ( - ALGOS, - load_hyperparams, - set_seeds, - get_device, - make_policy, - plot_eval_callback, - flatten_hyperparameters, -) -from shared.stats import EpisodesStats - - -@dataclass -class TrainArgs(RunArgs): - wandb_project_name: Optional[str] = None - wandb_entity: Optional[str] = None - wandb_tags: Sequence[str] = dataclasses.field(default_factory=list) - - -def train(args: TrainArgs): - print(args) - hyperparams = load_hyperparams(args.algo, args.env, os.getcwd()) - print(hyperparams) - config = Config(args, hyperparams, os.getcwd()) - - wandb_enabled = args.wandb_project_name - if wandb_enabled: - wandb.tensorboard.patch( - root_logdir=config.tensorboard_summary_path, pytorch=True - ) - wandb.init( - project=args.wandb_project_name, - entity=args.wandb_entity, - config=hyperparams, # type: ignore - name=config.run_name, - monitor_gym=True, - save_code=True, - tags=args.wandb_tags, - ) - wandb.config.update(args) - - tb_writer = SummaryWriter(config.tensorboard_summary_path) - - set_seeds(args.seed, args.use_deterministic_algorithms) - - env = make_env(config, tb_writer=tb_writer, **config.env_hyperparams) - device = get_device(config.device, env) - policy = make_policy(args.algo, env, device, **config.policy_hyperparams) - algo = ALGOS[args.algo](policy, env, device, tb_writer, **config.algo_hyperparams) - - eval_env = make_eval_env(config, **config.env_hyperparams) - record_best_videos = config.eval_params.get("record_best_videos", True) - callback = EvalCallback( - policy, - eval_env, - tb_writer, - best_model_path=config.model_dir_path(best=True), - **config.eval_params, - video_env=make_eval_env(config, override_n_envs=1, **config.env_hyperparams) - if record_best_videos - else None, - best_video_dir=config.best_videos_dir, - ) - algo.learn(config.n_timesteps, callback=callback) - - policy.save(config.model_dir_path(best=False)) - - eval_stats = callback.evaluate(n_episodes=10, print_returns=True) - - plot_eval_callback(callback, tb_writer, config.run_name) - - log_dict: Dict[str, Any] = { - "eval": eval_stats._asdict(), - } - if callback.best: - log_dict["best_eval"] = callback.best._asdict() - log_dict.update(hyperparams) - log_dict.update(vars(args)) - with open(config.logs_path, "a") as f: - yaml.dump({config.run_name: log_dict}, f) - - best_eval_stats: EpisodesStats = callback.best # type: ignore - tb_writer.add_hparams( - flatten_hyperparameters(hyperparams, vars(args)), - { - "hparam/best_mean": best_eval_stats.score.mean, - "hparam/best_result": best_eval_stats.score.mean - - best_eval_stats.score.std, - "hparam/last_mean": eval_stats.score.mean, - "hparam/last_result": eval_stats.score.mean - eval_stats.score.std, - }, - None, - config.run_name, - ) - - tb_writer.close() - - if wandb_enabled: - shutil.make_archive( - os.path.join(wandb.run.dir, config.model_dir_name()), - "zip", - config.model_dir_path(), - ) - shutil.make_archive( - os.path.join(wandb.run.dir, config.model_dir_name(best=True)), - "zip", - config.model_dir_path(best=True), - ) - wandb.finish() diff --git a/shared/algorithm.py b/shared/algorithm.py deleted file mode 100644 index 925b20c3c96203c5cc47b89c379ff633138055e7..0000000000000000000000000000000000000000 --- a/shared/algorithm.py +++ /dev/null @@ -1,35 +0,0 @@ -import gym -import torch - -from abc import ABC, abstractmethod -from stable_baselines3.common.vec_env.base_vec_env import VecEnv -from torch.utils.tensorboard.writer import SummaryWriter -from typing import List, Optional, TypeVar - -from shared.callbacks.callback import Callback -from shared.policy.policy import Policy -from shared.stats import EpisodesStats - -AlgorithmSelf = TypeVar("AlgorithmSelf", bound="Algorithm") - -class Algorithm(ABC): - @abstractmethod - def __init__( - self, - policy: Policy, - env: VecEnv, - device: torch.device, - tb_writer: SummaryWriter, - **kwargs, - ) -> None: - super().__init__() - self.policy = policy - self.env = env - self.device = device - self.tb_writer = tb_writer - - @abstractmethod - def learn( - self: AlgorithmSelf, total_timesteps: int, callback: Optional[Callback] = None - ) -> AlgorithmSelf: - ... diff --git a/shared/callbacks/callback.py b/shared/callbacks/callback.py deleted file mode 100644 index f53784b7070d680817f6b8d9fc1fd13085289b89..0000000000000000000000000000000000000000 --- a/shared/callbacks/callback.py +++ /dev/null @@ -1,12 +0,0 @@ -from abc import ABC, abstractmethod - - -class Callback(ABC): - - def __init__(self) -> None: - super().__init__() - self.timesteps_elapsed = 0 - - def on_step(self, timesteps_elapsed: int = 1) -> bool: - self.timesteps_elapsed += timesteps_elapsed - return True \ No newline at end of file diff --git a/shared/callbacks/eval_callback.py b/shared/callbacks/eval_callback.py deleted file mode 100644 index 9d89e92281e21609d53d41f0aa84edf26103e295..0000000000000000000000000000000000000000 --- a/shared/callbacks/eval_callback.py +++ /dev/null @@ -1,181 +0,0 @@ -import itertools -import numpy as np -import os - -from copy import deepcopy -from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvWrapper -from stable_baselines3.common.vec_env.vec_normalize import VecNormalize -from torch.utils.tensorboard.writer import SummaryWriter -from typing import List, Optional, Union - -from shared.callbacks.callback import Callback -from shared.policy.policy import Policy -from shared.stats import Episode, EpisodeAccumulator, EpisodesStats -from wrappers.vec_episode_recorder import VecEpisodeRecorder - - -class EvaluateAccumulator(EpisodeAccumulator): - def __init__(self, num_envs: int, goal_episodes: int, print_returns: bool = True): - super().__init__(num_envs) - self.completed_episodes_by_env_idx = [[] for _ in range(num_envs)] - self.goal_episodes_per_env = int(np.ceil(goal_episodes / num_envs)) - self.print_returns = print_returns - - def on_done(self, ep_idx: int, episode: Episode) -> None: - if ( - len(self.completed_episodes_by_env_idx[ep_idx]) - >= self.goal_episodes_per_env - ): - return - self.completed_episodes_by_env_idx[ep_idx].append(episode) - if self.print_returns: - print( - f"Episode {len(self)} | " - f"Score {episode.score} | " - f"Length {episode.length}" - ) - - def __len__(self) -> int: - return sum(len(ce) for ce in self.completed_episodes_by_env_idx) - - @property - def episodes(self) -> List[Episode]: - return list(itertools.chain(*self.completed_episodes_by_env_idx)) - - def is_done(self) -> bool: - return all( - len(ce) == self.goal_episodes_per_env - for ce in self.completed_episodes_by_env_idx - ) - - -def evaluate( - env: VecEnv, - policy: Policy, - n_episodes: int, - render: bool = False, - deterministic: bool = True, - print_returns: bool = True, -) -> EpisodesStats: - policy.eval() - episodes = EvaluateAccumulator(env.num_envs, n_episodes, print_returns) - - obs = env.reset() - while not episodes.is_done(): - act = policy.act(obs, deterministic=deterministic) - obs, rew, done, _ = env.step(act) - episodes.step(rew, done) - if render: - env.render() - stats = EpisodesStats(episodes.episodes) - if print_returns: - print(stats) - return stats - - -class EvalCallback(Callback): - def __init__( - self, - policy: Policy, - env: VecEnv, - tb_writer: SummaryWriter, - best_model_path: Optional[str] = None, - step_freq: Union[int, float] = 50_000, - n_episodes: int = 10, - save_best: bool = True, - deterministic: bool = True, - record_best_videos: bool = True, - video_env: Optional[VecEnv] = None, - best_video_dir: Optional[str] = None, - max_video_length: int = 3600, - ) -> None: - super().__init__() - self.policy = policy - self.env = env - self.tb_writer = tb_writer - self.best_model_path = best_model_path - self.step_freq = int(step_freq) - self.n_episodes = n_episodes - self.save_best = save_best - self.deterministic = deterministic - self.stats: List[EpisodesStats] = [] - self.best = None - - self.record_best_videos = record_best_videos - assert video_env or not record_best_videos - self.video_env = video_env - assert best_video_dir or not record_best_videos - self.best_video_dir = best_video_dir - if best_video_dir: - os.makedirs(best_video_dir, exist_ok=True) - self.max_video_length = max_video_length - self.best_video_base_path = None - - def on_step(self, timesteps_elapsed: int = 1) -> bool: - super().on_step(timesteps_elapsed) - if self.timesteps_elapsed // self.step_freq >= len(self.stats): - sync_vec_normalize(self.policy.vec_normalize, self.env) - self.evaluate() - return True - - def evaluate( - self, n_episodes: Optional[int] = None, print_returns: Optional[bool] = None - ) -> EpisodesStats: - eval_stat = evaluate( - self.env, - self.policy, - n_episodes or self.n_episodes, - deterministic=self.deterministic, - print_returns=print_returns or False, - ) - self.policy.train(True) - print(f"Eval Timesteps: {self.timesteps_elapsed} | {eval_stat}") - - self.stats.append(eval_stat) - - if not self.best or eval_stat >= self.best: - strictly_better = not self.best or eval_stat > self.best - self.best = eval_stat - if self.save_best: - assert self.best_model_path - self.policy.save(self.best_model_path) - print("Saved best model") - self.best.write_to_tensorboard( - self.tb_writer, "best_eval", self.timesteps_elapsed - ) - if strictly_better and self.record_best_videos: - assert self.video_env and self.best_video_dir - sync_vec_normalize(self.policy.vec_normalize, self.video_env) - self.best_video_base_path = os.path.join( - self.best_video_dir, str(self.timesteps_elapsed) - ) - video_wrapped = VecEpisodeRecorder( - self.video_env, - self.best_video_base_path, - max_video_length=self.max_video_length, - ) - video_stats = evaluate( - video_wrapped, - self.policy, - 1, - deterministic=self.deterministic, - print_returns=False, - ) - print(f"Saved best video: {video_stats}") - - eval_stat.write_to_tensorboard(self.tb_writer, "eval", self.timesteps_elapsed) - - return eval_stat - - -def sync_vec_normalize( - origin_vec_normalize: Optional[VecNormalize], destination_env: VecEnv -) -> None: - if origin_vec_normalize is not None: - eval_env_wrapper = destination_env - while isinstance(eval_env_wrapper, VecEnvWrapper): - if isinstance(eval_env_wrapper, VecNormalize): - if hasattr(origin_vec_normalize, "obs_rms"): - eval_env_wrapper.obs_rms = deepcopy(origin_vec_normalize.obs_rms) - eval_env_wrapper.ret_rms = deepcopy(origin_vec_normalize.ret_rms) - eval_env_wrapper = eval_env_wrapper.venv diff --git a/shared/module.py b/shared/module.py deleted file mode 100644 index 0760240b608fc603ac07d13ca11eaea7a7353f63..0000000000000000000000000000000000000000 --- a/shared/module.py +++ /dev/null @@ -1,121 +0,0 @@ -import gym -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - -from gym.spaces import Box, Discrete -from stable_baselines3.common.preprocessing import get_flattened_obs_dim -from typing import Sequence, Type - - -class FeatureExtractor(nn.Module): - def __init__( - self, - obs_space: gym.Space, - activation: Type[nn.Module], - init_layers_orthogonal: bool = False, - cnn_feature_dim: int = 512, - ) -> None: - super().__init__() - if isinstance(obs_space, Box): - # Conv2D: (channels, height, width) - if len(obs_space.shape) == 3: - # CNN from DQN Nature paper: Mnih, Volodymyr, et al. - # "Human-level control through deep reinforcement learning." - # Nature 518.7540 (2015): 529-533. - cnn = nn.Sequential( - layer_init( - nn.Conv2d(obs_space.shape[0], 32, kernel_size=8, stride=4), - init_layers_orthogonal, - ), - activation(), - layer_init( - nn.Conv2d(32, 64, kernel_size=4, stride=2), - init_layers_orthogonal, - ), - activation(), - layer_init( - nn.Conv2d(64, 64, kernel_size=3, stride=1), - init_layers_orthogonal, - ), - activation(), - nn.Flatten(), - ) - - def preprocess(obs: torch.Tensor) -> torch.Tensor: - if len(obs.shape) == 3: - obs = obs.unsqueeze(0) - return obs.float() / 255.0 - - with torch.no_grad(): - cnn_out = cnn(preprocess(torch.as_tensor(obs_space.sample()))) - self.preprocess = preprocess - self.feature_extractor = nn.Sequential( - cnn, - layer_init( - nn.Linear(cnn_out.shape[1], cnn_feature_dim), - init_layers_orthogonal, - ), - activation(), - ) - self.out_dim = cnn_feature_dim - elif len(obs_space.shape) == 1: - - def preprocess(obs: torch.Tensor) -> torch.Tensor: - if len(obs.shape) == 1: - obs = obs.unsqueeze(0) - return obs.float() - - self.preprocess = preprocess - self.feature_extractor = nn.Flatten() - self.out_dim = get_flattened_obs_dim(obs_space) - else: - raise ValueError(f"Unsupported observation space: {obs_space}") - elif isinstance(obs_space, Discrete): - self.preprocess = lambda x: F.one_hot(x, obs_space.n).float() - self.feature_extractor = nn.Flatten() - self.out_dim = obs_space.n - else: - raise NotImplementedError - - def forward(self, obs: torch.Tensor) -> torch.Tensor: - if self.preprocess: - obs = self.preprocess(obs) - return self.feature_extractor(obs) - - -def mlp( - layer_sizes: Sequence[int], - activation: Type[nn.Module], - output_activation: Type[nn.Module] = nn.Identity, - init_layers_orthogonal: bool = False, - final_layer_gain: float = np.sqrt(2), -) -> nn.Module: - layers = [] - for i in range(len(layer_sizes) - 2): - layers.append( - layer_init( - nn.Linear(layer_sizes[i], layer_sizes[i + 1]), init_layers_orthogonal - ) - ) - layers.append(activation()) - layers.append( - layer_init( - nn.Linear(layer_sizes[-2], layer_sizes[-1]), - init_layers_orthogonal, - std=final_layer_gain, - ) - ) - layers.append(output_activation()) - return nn.Sequential(*layers) - - -def layer_init( - layer: nn.Module, init_layers_orthogonal: bool, std: float = np.sqrt(2) -) -> nn.Module: - if not init_layers_orthogonal: - return layer - nn.init.orthogonal_(layer.weight, std) # type: ignore - nn.init.constant_(layer.bias, 0.0) # type: ignore - return layer diff --git a/shared/policy/actor.py b/shared/policy/actor.py deleted file mode 100644 index ea732cbcc99c4599df0d063da54a60d0d9363e8c..0000000000000000000000000000000000000000 --- a/shared/policy/actor.py +++ /dev/null @@ -1,304 +0,0 @@ -import gym -import torch -import torch.nn as nn - -from abc import ABC, abstractmethod -from gym.spaces import Box, Discrete -from torch.distributions import Categorical, Distribution, Normal -from typing import NamedTuple, Optional, Sequence, Type, TypeVar, Union - -from shared.module import FeatureExtractor, mlp - - -class PiForward(NamedTuple): - pi: Distribution - logp_a: Optional[torch.Tensor] - entropy: Optional[torch.Tensor] - - -class Actor(nn.Module, ABC): - @abstractmethod - def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward: - ... - - -class CategoricalActorHead(Actor): - def __init__( - self, - act_dim: int, - hidden_sizes: Sequence[int] = (32,), - activation: Type[nn.Module] = nn.Tanh, - init_layers_orthogonal: bool = True, - ) -> None: - super().__init__() - layer_sizes = tuple(hidden_sizes) + (act_dim,) - self._fc = mlp( - layer_sizes, - activation, - init_layers_orthogonal=init_layers_orthogonal, - final_layer_gain=0.01, - ) - - def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward: - logits = self._fc(obs) - pi = Categorical(logits=logits) - logp_a = None - entropy = None - if a is not None: - logp_a = pi.log_prob(a) - entropy = pi.entropy() - return PiForward(pi, logp_a, entropy) - - -class GaussianDistribution(Normal): - def log_prob(self, a: torch.Tensor) -> torch.Tensor: - return super().log_prob(a).sum(axis=-1) - - def sample(self) -> torch.Tensor: - return self.rsample() - - -class GaussianActorHead(Actor): - def __init__( - self, - act_dim: int, - hidden_sizes: Sequence[int] = (32,), - activation: Type[nn.Module] = nn.Tanh, - init_layers_orthogonal: bool = True, - log_std_init: float = -0.5, - ) -> None: - super().__init__() - layer_sizes = tuple(hidden_sizes) + (act_dim,) - self.mu_net = mlp( - layer_sizes, - activation, - init_layers_orthogonal=init_layers_orthogonal, - final_layer_gain=0.01, - ) - self.log_std = nn.Parameter( - torch.ones(act_dim, dtype=torch.float32) * log_std_init - ) - - def _distribution(self, obs: torch.Tensor) -> Distribution: - mu = self.mu_net(obs) - std = torch.exp(self.log_std) - return GaussianDistribution(mu, std) - - def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward: - pi = self._distribution(obs) - logp_a = None - entropy = None - if a is not None: - logp_a = pi.log_prob(a) - entropy = pi.entropy() - return PiForward(pi, logp_a, entropy) - - -class TanhBijector: - def __init__(self, epsilon: float = 1e-6) -> None: - self.epsilon = epsilon - - @staticmethod - def forward(x: torch.Tensor) -> torch.Tensor: - return torch.tanh(x) - - @staticmethod - def inverse(y: torch.Tensor) -> torch.Tensor: - eps = torch.finfo(y.dtype).eps - clamped_y = y.clamp(min=-1.0 + eps, max=1.0 - eps) - return torch.atanh(clamped_y) - - def log_prob_correction(self, x: torch.Tensor) -> torch.Tensor: - return torch.log(1.0 - torch.tanh(x) ** 2 + self.epsilon) - - -class StateDependentNoiseDistribution(Normal): - def __init__( - self, - loc, - scale, - latent_sde: torch.Tensor, - exploration_mat: torch.Tensor, - exploration_matrices: torch.Tensor, - bijector: Optional[TanhBijector] = None, - validate_args=None, - ): - super().__init__(loc, scale, validate_args) - self.latent_sde = latent_sde - self.exploration_mat = exploration_mat - self.exploration_matrices = exploration_matrices - self.bijector = bijector - - def log_prob(self, a: torch.Tensor) -> torch.Tensor: - gaussian_a = self.bijector.inverse(a) if self.bijector else a - log_prob = super().log_prob(gaussian_a).sum(axis=-1) - if self.bijector: - log_prob -= torch.sum(self.bijector.log_prob_correction(gaussian_a), dim=1) - return log_prob - - def sample(self) -> torch.Tensor: - noise = self._get_noise() - actions = self.mean + noise - return self.bijector.forward(actions) if self.bijector else actions - - def _get_noise(self) -> torch.Tensor: - if len(self.latent_sde) == 1 or len(self.latent_sde) != len( - self.exploration_matrices - ): - return torch.mm(self.latent_sde, self.exploration_mat) - # (batch_size, n_features) -> (batch_size, 1, n_features) - latent_sde = self.latent_sde.unsqueeze(dim=1) - # (batch_size, 1, n_actions) - noise = torch.bmm(latent_sde, self.exploration_matrices) - return noise.squeeze(dim=1) - - @property - def mode(self) -> torch.Tensor: - mean = super().mode - return self.bijector.forward(mean) if self.bijector else mean - - -StateDependentNoiseActorHeadSelf = TypeVar( - "StateDependentNoiseActorHeadSelf", bound="StateDependentNoiseActorHead" -) - - -class StateDependentNoiseActorHead(Actor): - def __init__( - self, - act_dim: int, - hidden_sizes: Sequence[int] = (32,), - activation: Type[nn.Module] = nn.Tanh, - init_layers_orthogonal: bool = True, - log_std_init: float = -0.5, - full_std: bool = True, - squash_output: bool = False, - learn_std: bool = False, - ) -> None: - super().__init__() - self.act_dim = act_dim - layer_sizes = tuple(hidden_sizes) + (self.act_dim,) - if len(layer_sizes) == 2: - self.latent_net = nn.Identity() - elif len(layer_sizes) > 2: - self.latent_net = mlp( - layer_sizes[:-1], - activation, - output_activation=activation, - init_layers_orthogonal=init_layers_orthogonal, - ) - else: - raise ValueError("hidden_sizes must be of at least length 1") - self.mu_net = mlp( - layer_sizes[-2:], - activation, - init_layers_orthogonal=init_layers_orthogonal, - final_layer_gain=0.01, - ) - self.full_std = full_std - std_dim = (hidden_sizes[-1], act_dim if self.full_std else 1) - self.log_std = nn.Parameter( - torch.ones(std_dim, dtype=torch.float32) * log_std_init - ) - self.bijector = TanhBijector() if squash_output else None - self.learn_std = learn_std - self.device = None - - self.exploration_mat = None - self.exploration_matrices = None - self.sample_weights() - - def to( - self: StateDependentNoiseActorHeadSelf, - device: Optional[torch.device] = None, - dtype: Optional[Union[torch.dtype, str]] = None, - non_blocking: bool = False, - ) -> StateDependentNoiseActorHeadSelf: - super().to(device, dtype, non_blocking) - self.device = device - return self - - def _distribution(self, obs: torch.Tensor) -> Distribution: - latent = self.latent_net(obs) - mu = self.mu_net(latent) - latent_sde = latent if self.learn_std else latent.detach() - variance = torch.mm(latent_sde**2, self._get_std() ** 2) - assert self.exploration_mat is not None - assert self.exploration_matrices is not None - return StateDependentNoiseDistribution( - mu, - torch.sqrt(variance + 1e-6), - latent_sde, - self.exploration_mat, - self.exploration_matrices, - self.bijector, - ) - - def _get_std(self) -> torch.Tensor: - std = torch.exp(self.log_std) - if self.full_std: - return std - ones = torch.ones(self.log_std.shape[0], self.act_dim) - if self.device: - ones = ones.to(self.device) - return ones * std - - def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward: - pi = self._distribution(obs) - logp_a = None - entropy = None - if a is not None: - logp_a = pi.log_prob(a) - entropy = -logp_a - return PiForward(pi, logp_a, entropy) - - def sample_weights(self, batch_size: int = 1) -> None: - std = self._get_std() - weights_dist = Normal(torch.zeros_like(std), std) - # Reparametrization trick to pass gradients - self.exploration_mat = weights_dist.rsample() - self.exploration_matrices = weights_dist.rsample(torch.Size((batch_size,))) - - -def actor_head( - action_space: gym.Space, - hidden_sizes: Sequence[int], - init_layers_orthogonal: bool, - activation: Type[nn.Module], - log_std_init: float = -0.5, - use_sde: bool = False, - full_std: bool = True, - squash_output: bool = False, -) -> Actor: - assert not use_sde or isinstance( - action_space, Box - ), "use_sde only valid if Box action_space" - assert not squash_output or use_sde, "squash_output only valid if use_sde" - if isinstance(action_space, Discrete): - return CategoricalActorHead( - action_space.n, - hidden_sizes=hidden_sizes, - activation=activation, - init_layers_orthogonal=init_layers_orthogonal, - ) - elif isinstance(action_space, Box): - if use_sde: - return StateDependentNoiseActorHead( - action_space.shape[0], - hidden_sizes=hidden_sizes, - activation=activation, - init_layers_orthogonal=init_layers_orthogonal, - log_std_init=log_std_init, - full_std=full_std, - squash_output=squash_output, - ) - else: - return GaussianActorHead( - action_space.shape[0], - hidden_sizes=hidden_sizes, - activation=activation, - init_layers_orthogonal=init_layers_orthogonal, - log_std_init=log_std_init, - ) - else: - raise ValueError(f"Unsupported action space: {action_space}") diff --git a/shared/policy/critic.py b/shared/policy/critic.py deleted file mode 100644 index ae687ead3c453572f07087891bfc420a20b56470..0000000000000000000000000000000000000000 --- a/shared/policy/critic.py +++ /dev/null @@ -1,27 +0,0 @@ -import gym -import torch -import torch.nn as nn - -from typing import Sequence, Type -from shared.module import FeatureExtractor, mlp - - -class CriticHead(nn.Module): - def __init__( - self, - hidden_sizes: Sequence[int] = (32,), - activation: Type[nn.Module] = nn.Tanh, - init_layers_orthogonal: bool = True, - ) -> None: - super().__init__() - layer_sizes = tuple(hidden_sizes) + (1,) - self._fc = mlp( - layer_sizes, - activation, - init_layers_orthogonal=init_layers_orthogonal, - final_layer_gain=1.0, - ) - - def forward(self, obs: torch.Tensor) -> torch.Tensor: - v = self._fc(obs) - return v.squeeze(-1) diff --git a/shared/policy/on_policy.py b/shared/policy/on_policy.py deleted file mode 100644 index 507dff50a867d28024b299b9a1047a0edcb499b4..0000000000000000000000000000000000000000 --- a/shared/policy/on_policy.py +++ /dev/null @@ -1,177 +0,0 @@ -import gym -import numpy as np -import torch - -from gym.spaces import Box -from pathlib import Path -from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs -from typing import NamedTuple, Optional, Sequence, Tuple, TypeVar - -from shared.module import FeatureExtractor -from shared.policy.actor import PiForward, StateDependentNoiseActorHead, actor_head -from shared.policy.critic import CriticHead -from shared.policy.policy import ACTIVATION, Policy - - -class Step(NamedTuple): - a: np.ndarray - v: np.ndarray - logp_a: np.ndarray - clamped_a: np.ndarray - - -class ACForward(NamedTuple): - logp_a: torch.Tensor - entropy: torch.Tensor - v: torch.Tensor - - -FEAT_EXT_FILE_NAME = "feat_ext.pt" -V_FEAT_EXT_FILE_NAME = "v_feat_ext.pt" -PI_FILE_NAME = "pi.pt" -V_FILE_NAME = "v.pt" -ActorCriticSelf = TypeVar("ActorCriticSelf", bound="ActorCritic") - - -def clamp_actions( - actions: np.ndarray, action_space: gym.Space, squash_output: bool -) -> np.ndarray: - if isinstance(action_space, Box): - low, high = action_space.low, action_space.high # type: ignore - if squash_output: - # Squashed output is already between -1 and 1. Rescale if the actual - # output needs to something other than -1 and 1 - return low + 0.5 * (actions + 1) * (high - low) - else: - return np.clip(actions, low, high) - return actions - - -class ActorCritic(Policy): - def __init__( - self, - env: VecEnv, - pi_hidden_sizes: Sequence[int], - v_hidden_sizes: Sequence[int], - init_layers_orthogonal: bool = True, - activation_fn: str = "tanh", - log_std_init: float = -0.5, - use_sde: bool = False, - full_std: bool = True, - squash_output: bool = False, - share_features_extractor: bool = True, - cnn_feature_dim: int = 512, - **kwargs, - ) -> None: - super().__init__(env, **kwargs) - activation = ACTIVATION[activation_fn] - observation_space = env.observation_space - self.action_space = env.action_space - self.squash_output = squash_output - self.share_features_extractor = share_features_extractor - self._feature_extractor = FeatureExtractor( - observation_space, - activation, - init_layers_orthogonal=init_layers_orthogonal, - cnn_feature_dim=cnn_feature_dim, - ) - self._pi = actor_head( - self.action_space, - (self._feature_extractor.out_dim,) + tuple(pi_hidden_sizes), - init_layers_orthogonal, - activation, - log_std_init=log_std_init, - use_sde=use_sde, - full_std=full_std, - squash_output=squash_output, - ) - - if not share_features_extractor: - self._v_feature_extractor = FeatureExtractor( - observation_space, - activation, - init_layers_orthogonal=init_layers_orthogonal, - cnn_feature_dim=cnn_feature_dim, - ) - v_hidden_sizes = (self._v_feature_extractor.out_dim,) + tuple( - v_hidden_sizes - ) - else: - self._v_feature_extractor = None - v_hidden_sizes = (self._feature_extractor.out_dim,) + tuple(v_hidden_sizes) - self._v = CriticHead( - hidden_sizes=v_hidden_sizes, - activation=activation, - init_layers_orthogonal=init_layers_orthogonal, - ) - - def _pi_forward( - self, obs: torch.Tensor, action: Optional[torch.Tensor] = None - ) -> Tuple[PiForward, torch.Tensor]: - p_fe = self._feature_extractor(obs) - pi_forward = self._pi(p_fe, action) - - return pi_forward, p_fe - - def _v_forward(self, obs: torch.Tensor, p_fc: torch.Tensor) -> torch.Tensor: - v_fe = self._v_feature_extractor(obs) if self._v_feature_extractor else p_fc - return self._v(v_fe) - - def forward(self, obs: torch.Tensor, action: torch.Tensor) -> ACForward: - (_, logp_a, entropy), p_fc = self._pi_forward(obs, action) - v = self._v_forward(obs, p_fc) - - assert logp_a is not None - assert entropy is not None - return ACForward(logp_a, entropy, v) - - def _as_tensor(self, obs: VecEnvObs) -> torch.Tensor: - assert isinstance(obs, np.ndarray) - o = torch.as_tensor(obs) - if self.device is not None: - o = o.to(self.device) - return o - - def value(self, obs: VecEnvObs) -> np.ndarray: - o = self._as_tensor(obs) - with torch.no_grad(): - fe = ( - self._v_feature_extractor(o) - if self._v_feature_extractor - else self._feature_extractor(o) - ) - v = self._v(fe) - return v.cpu().numpy() - - def step(self, obs: VecEnvObs) -> Step: - o = self._as_tensor(obs) - with torch.no_grad(): - (pi, _, _), p_fc = self._pi_forward(o) - a = pi.sample() - logp_a = pi.log_prob(a) - - v = self._v_forward(o, p_fc) - - a_np = a.cpu().numpy() - clamped_a_np = clamp_actions(a_np, self.action_space, self.squash_output) - return Step(a_np, v.cpu().numpy(), logp_a.cpu().numpy(), clamped_a_np) - - def act(self, obs: np.ndarray, deterministic: bool = True) -> np.ndarray: - if not deterministic: - return self.step(obs).clamped_a - else: - o = self._as_tensor(obs) - with torch.no_grad(): - (pi, _, _), _ = self._pi_forward(o) - a = pi.mode - return clamp_actions(a.cpu().numpy(), self.action_space, self.squash_output) - - def load(self, path: str) -> None: - super().load(path) - self.reset_noise() - - def reset_noise(self, batch_size: Optional[int] = None) -> None: - if isinstance(self._pi, StateDependentNoiseActorHead): - self._pi.sample_weights( - batch_size=batch_size if batch_size else self.env.num_envs - ) diff --git a/shared/policy/policy.py b/shared/policy/policy.py deleted file mode 100644 index 46fd6fdc3dbf54dade97e3601fdfa25aa1fb4f08..0000000000000000000000000000000000000000 --- a/shared/policy/policy.py +++ /dev/null @@ -1,61 +0,0 @@ -import numpy as np -import os -import torch -import torch.nn as nn - -from abc import ABC, abstractmethod -from stable_baselines3.common.vec_env import unwrap_vec_normalize -from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs -from typing import Dict, Optional, Type, TypeVar, Union - -ACTIVATION: Dict[str, Type[nn.Module]] = { - "tanh": nn.Tanh, - "relu": nn.ReLU, -} - -VEC_NORMALIZE_FILENAME = "vecnormalize.pkl" -MODEL_FILENAME = "model.pth" - -PolicySelf = TypeVar("PolicySelf", bound="Policy") - - -class Policy(nn.Module, ABC): - @abstractmethod - def __init__(self, env: VecEnv, **kwargs) -> None: - super().__init__() - self.env = env - self.vec_normalize = unwrap_vec_normalize(env) - self.device = None - - def to( - self: PolicySelf, - device: Optional[torch.device] = None, - dtype: Optional[Union[torch.dtype, str]] = None, - non_blocking: bool = False, - ) -> PolicySelf: - super().to(device, dtype, non_blocking) - self.device = device - return self - - @abstractmethod - def act(self, obs: VecEnvObs, deterministic: bool = True) -> np.ndarray: - ... - - def save(self, path: str) -> None: - os.makedirs(path, exist_ok=True) - - if self.vec_normalize: - self.vec_normalize.save(os.path.join(path, VEC_NORMALIZE_FILENAME)) - torch.save( - self.state_dict(), - os.path.join(path, MODEL_FILENAME), - ) - - def load(self, path: str) -> None: - # VecNormalize load occurs in env.py - self.load_state_dict( - torch.load(os.path.join(path, MODEL_FILENAME), map_location=self.device) - ) - - def reset_noise(self) -> None: - pass diff --git a/shared/schedule.py b/shared/schedule.py deleted file mode 100644 index 94458a32e4cdbc42d305c6d13ba8e07d9f7686c0..0000000000000000000000000000000000000000 --- a/shared/schedule.py +++ /dev/null @@ -1,19 +0,0 @@ -from typing import Callable - -Schedule = Callable[[float], float] - - -def linear_schedule( - start_val: float, end_val: float, end_fraction: float = 1.0 -) -> Schedule: - def func(progress_fraction: float) -> float: - if progress_fraction >= end_fraction: - return end_val - else: - return start_val + (end_val - start_val) * progress_fraction / end_fraction - - return func - - -def constant_schedule(val: float) -> Schedule: - return lambda f: val diff --git a/shared/stats.py b/shared/stats.py deleted file mode 100644 index 4d5ab9ecaf68493d6932c85b3b06a001c1d3df47..0000000000000000000000000000000000000000 --- a/shared/stats.py +++ /dev/null @@ -1,152 +0,0 @@ -import numpy as np - -from dataclasses import dataclass -from torch.utils.tensorboard.writer import SummaryWriter -from typing import Dict, List, Optional, Sequence, TypeVar - - -@dataclass -class Episode: - score: float = 0 - length: int = 0 - - -StatisticSelf = TypeVar("StatisticSelf", bound="Statistic") - - -@dataclass -class Statistic: - values: np.ndarray - round_digits: int = 2 - - @property - def mean(self) -> float: - return np.mean(self.values).item() - - @property - def std(self) -> float: - return np.std(self.values).item() - - @property - def min(self) -> float: - return np.min(self.values).item() - - @property - def max(self) -> float: - return np.max(self.values).item() - - def sum(self) -> float: - return np.sum(self.values).item() - - def __len__(self) -> int: - return len(self.values) - - def _diff(self: StatisticSelf, o: StatisticSelf) -> float: - return (self.mean - self.std) - (o.mean - o.std) - - def __gt__(self: StatisticSelf, o: StatisticSelf) -> bool: - return self._diff(o) > 0 - - def __ge__(self: StatisticSelf, o: StatisticSelf) -> bool: - return self._diff(o) >= 0 - - def __repr__(self) -> str: - mean = round(self.mean, self.round_digits) - std = round(self.std, self.round_digits) - if self.round_digits == 0: - mean = int(mean) - std = int(std) - return f"{mean} +/- {std}" - - def to_dict(self) -> Dict[str, float]: - return { - "mean": self.mean, - "std": self.std, - "min": self.min, - "max": self.max, - } - - -EpisodesStatsSelf = TypeVar("EpisodesStatsSelf", bound="EpisodesStats") - - -class EpisodesStats: - episodes: Sequence[Episode] - simple: bool - score: Statistic - length: Statistic - - def __init__(self, episodes: Sequence[Episode], simple: bool = False) -> None: - self.episodes = episodes - self.simple = simple - self.score = Statistic(np.array([e.score for e in episodes])) - self.length = Statistic(np.array([e.length for e in episodes]), round_digits=0) - - def __gt__(self: EpisodesStatsSelf, o: EpisodesStatsSelf) -> bool: - return self.score > o.score - - def __ge__(self: EpisodesStatsSelf, o: EpisodesStatsSelf) -> bool: - return self.score >= o.score - - def __repr__(self) -> str: - return ( - f"Score: {self.score} ({round(self.score.mean - self.score.std, 2)}) | " - f"Length: {self.length}" - ) - - def __len__(self) -> int: - return len(self.episodes) - - def _asdict(self) -> dict: - return { - "n_episodes": len(self.episodes), - "score": self.score.to_dict(), - "length": self.length.to_dict(), - } - - def write_to_tensorboard( - self, tb_writer: SummaryWriter, main_tag: str, global_step: Optional[int] = None - ) -> None: - stats = {"mean": self.score.mean} - if not self.simple: - stats.update( - { - "min": self.score.min, - "max": self.score.max, - "result": self.score.mean - self.score.std, - "n_episodes": len(self.episodes), - } - ) - tb_writer.add_scalars( - main_tag, - stats, - global_step=global_step, - ) - - -class EpisodeAccumulator: - def __init__(self, num_envs: int): - self._episodes = [] - self.current_episodes = [Episode() for _ in range(num_envs)] - - @property - def episodes(self) -> List[Episode]: - return self._episodes - - def step(self, reward: np.ndarray, done: np.ndarray) -> None: - for idx, current in enumerate(self.current_episodes): - current.score += reward[idx] - current.length += 1 - if done[idx]: - self._episodes.append(current) - self.on_done(idx, current) - self.current_episodes[idx] = Episode() - - def __len__(self) -> int: - return len(self.episodes) - - def on_done(self, ep_idx: int, episode: Episode) -> None: - pass - - def stats(self) -> EpisodesStats: - return EpisodesStats(self.episodes) diff --git a/shared/trajectory.py b/shared/trajectory.py deleted file mode 100644 index 788c45400c06acbca2393a205d4d67ea0db1ca5c..0000000000000000000000000000000000000000 --- a/shared/trajectory.py +++ /dev/null @@ -1,30 +0,0 @@ -import numpy as np -import torch - -from dataclasses import dataclass -from typing import List - - -@dataclass -class Trajectory: - obs: List[np.ndarray] - act: List[np.ndarray] - rew: List[float] - v: List[float] - terminated: bool - - def __init__(self) -> None: - self.obs = [] - self.act = [] - self.rew = [] - self.v = [] - self.terminated = False - - def add(self, obs: np.ndarray, act: np.ndarray, rew: float, v: float): - self.obs.append(obs) - self.act.append(act) - self.rew.append(rew) - self.v.append(v) - - def __len__(self) -> int: - return len(self.obs) diff --git a/shared/utils.py b/shared/utils.py deleted file mode 100644 index 3f0ef0ad04c80a4fd8849eb2e021041d120125a4..0000000000000000000000000000000000000000 --- a/shared/utils.py +++ /dev/null @@ -1,8 +0,0 @@ -import numpy as np - - -def discounted_cumsum(x: np.ndarray, gamma: float) -> np.ndarray: - dc = x.copy() - for i in reversed(range(len(x) - 1)): - dc[i] += gamma * dc[i + 1] - return dc diff --git a/vpg/policy.py b/vpg/policy.py deleted file mode 100644 index 966fa0e8ec071cbcfd96ab0339e7c2ccfd4465b0..0000000000000000000000000000000000000000 --- a/vpg/policy.py +++ /dev/null @@ -1,119 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn - -from gym.spaces import Box -from pathlib import Path -from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs -from typing import NamedTuple, Optional, Sequence, TypeVar - -from shared.module import FeatureExtractor -from shared.policy.actor import ( - PiForward, - Actor, - StateDependentNoiseActorHead, - actor_head, -) -from shared.policy.critic import CriticHead -from shared.policy.on_policy import Step, clamp_actions -from shared.policy.policy import ACTIVATION, Policy - -PI_FILE_NAME = "pi.pt" -V_FILE_NAME = "v.pt" - - -class VPGActor(Actor): - def __init__(self, feature_extractor: FeatureExtractor, head: Actor) -> None: - super().__init__() - self.feature_extractor = feature_extractor - self.head = head - - def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward: - fe = self.feature_extractor(obs) - return self.head(fe, a) - - -class VPGActorCritic(Policy): - def __init__( - self, - env: VecEnv, - hidden_sizes: Sequence[int], - init_layers_orthogonal: bool = True, - activation_fn: str = "tanh", - log_std_init: float = -0.5, - use_sde: bool = False, - full_std: bool = True, - squash_output: bool = False, - **kwargs, - ) -> None: - super().__init__(env, **kwargs) - activation = ACTIVATION[activation_fn] - obs_space = env.observation_space - self.action_space = env.action_space - self.use_sde = use_sde - self.squash_output = squash_output - - pi_feature_extractor = FeatureExtractor( - obs_space, activation, init_layers_orthogonal=init_layers_orthogonal - ) - pi_head = actor_head( - self.action_space, - (pi_feature_extractor.out_dim,) + tuple(hidden_sizes), - init_layers_orthogonal, - activation, - log_std_init=log_std_init, - use_sde=use_sde, - full_std=full_std, - squash_output=squash_output, - ) - self.pi = VPGActor(pi_feature_extractor, pi_head) - - v_feature_extractor = FeatureExtractor( - obs_space, activation, init_layers_orthogonal=init_layers_orthogonal - ) - v_head = CriticHead( - (v_feature_extractor.out_dim,) + tuple(hidden_sizes), - activation=activation, - init_layers_orthogonal=init_layers_orthogonal, - ) - self.v = nn.Sequential(v_feature_extractor, v_head) - - def _as_tensor(self, obs: VecEnvObs) -> torch.Tensor: - assert isinstance(obs, np.ndarray) - o = torch.as_tensor(obs) - if self.device is not None: - o = o.to(self.device) - return o - - def step(self, obs: VecEnvObs) -> Step: - o = self._as_tensor(obs) - with torch.no_grad(): - pi, _, _ = self.pi(o) - a = pi.sample() - logp_a = pi.log_prob(a) - - v = self.v(o) - - a_np = a.cpu().numpy() - clamped_a_np = clamp_actions(a_np, self.action_space, self.squash_output) - return Step(a_np, v.cpu().numpy(), logp_a.cpu().numpy(), clamped_a_np) - - def act(self, obs: np.ndarray, deterministic: bool = True) -> np.ndarray: - if not deterministic: - return self.step(obs).clamped_a - else: - o = self._as_tensor(obs) - with torch.no_grad(): - pi, _, _ = self.pi(o) - a = pi.mode - return clamp_actions(a.cpu().numpy(), self.action_space, self.squash_output) - - def load(self, path: str) -> None: - super().load(path) - self.reset_noise() - - def reset_noise(self, batch_size: Optional[int] = None) -> None: - if isinstance(self.pi.head, StateDependentNoiseActorHead): - self.pi.head.sample_weights( - batch_size=batch_size if batch_size else self.env.num_envs - ) diff --git a/vpg/vpg.py b/vpg/vpg.py deleted file mode 100644 index 57ae6cb858bfe2be8c30911771ef6fa383aba1f3..0000000000000000000000000000000000000000 --- a/vpg/vpg.py +++ /dev/null @@ -1,193 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn - -from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs -from torch.optim import Adam -from torch.utils.tensorboard.writer import SummaryWriter -from typing import Optional, Sequence, NamedTuple, TypeVar - -from shared.algorithm import Algorithm -from shared.callbacks.callback import Callback -from shared.trajectory import Trajectory -from shared.utils import discounted_cumsum -from vpg.policy import VPGActorCritic - - -class TrajectoryAccumulator: - def __init__(self, num_envs: int, goal_steps: int): - self.num_envs = num_envs - - self.trajectories = [] - self.current_trajectories = [Trajectory() for _ in range(num_envs)] - - self.steps_per_env = int(np.ceil(goal_steps / num_envs)) - self.step_idx = 0 - self.envs_done: set[int] = set() - - def step( - self, - obs: VecEnvObs, - action: np.ndarray, - reward: np.ndarray, - done: np.ndarray, - val: np.ndarray, - ) -> None: - assert isinstance(obs, np.ndarray) - self.step_idx += 1 - for i, trajectory in enumerate(self.current_trajectories): - trajectory.add(obs[i], action[i], reward[i], val[i]) - if done[i]: - # TODO: Eventually take advantage of terminated/truncated - # differentiation in later versions of gym. - trajectory.terminated = True - self.trajectories.append(trajectory) - self.current_trajectories[i] = Trajectory() - if self.step_idx >= self.steps_per_env: - self.envs_done.add(i) - - def is_done(self) -> bool: - return len(self.envs_done) == self.num_envs - - def n_timesteps(self) -> int: - return np.sum([len(t) for t in self.trajectories]).item() - - -class RtgAdvantage(NamedTuple): - rewards_to_go: torch.Tensor - advantage: torch.Tensor - - -class TrainEpochStats(NamedTuple): - pi_loss: float - v_loss: float - - def write_to_tensorboard(self, tb_writer: SummaryWriter, global_step: int) -> None: - tb_writer.add_scalars("losses", self._asdict(), global_step=global_step) - - -VanillaPolicyGradientSelf = TypeVar( - "VanillaPolicyGradientSelf", bound="VanillaPolicyGradient" -) - - -class VanillaPolicyGradient(Algorithm): - def __init__( - self, - policy: VPGActorCritic, - env: VecEnv, - device: torch.device, - tb_writer: SummaryWriter, - gamma: float = 0.99, - pi_lr: float = 3e-4, - val_lr: float = 1e-3, - train_v_iters: int = 80, - lam: float = 0.97, - max_grad_norm: float = 10.0, - steps_per_epoch: int = 4_000, - ) -> None: - super().__init__(policy, env, device, tb_writer) - self.policy = policy - - self.gamma = gamma - self.lam = lam - self.pi_optim = Adam(self.policy.pi.parameters(), lr=pi_lr) - self.val_optim = Adam(self.policy.v.parameters(), lr=val_lr) - self.max_grad_norm = max_grad_norm - - self.steps_per_epoch = steps_per_epoch - self.train_v_iters = train_v_iters - - def learn( - self: VanillaPolicyGradientSelf, - total_timesteps: int, - callback: Optional[Callback] = None, - ) -> VanillaPolicyGradientSelf: - self.policy.train(True) - obs = self.env.reset() - timesteps_elapsed = 0 - epoch_cnt = 0 - while timesteps_elapsed < total_timesteps: - epoch_cnt += 1 - accumulator = self._collect_trajectories(obs) - epoch_stats = self.train(accumulator.trajectories) - epoch_steps = accumulator.n_timesteps() - timesteps_elapsed += epoch_steps - epoch_stats.write_to_tensorboard( - self.tb_writer, global_step=timesteps_elapsed - ) - print( - f"Epoch: {epoch_cnt} | " - f"Pi Loss: {round(epoch_stats.pi_loss, 2)} | " - f"V Loss: {round(epoch_stats.v_loss, 2)} | " - f"Total Steps: {timesteps_elapsed}" - ) - if callback: - callback.on_step(timesteps_elapsed=epoch_steps) - return self - - def train(self, trajectories: Sequence[Trajectory]) -> TrainEpochStats: - obs = torch.as_tensor( - np.concatenate([np.array(t.obs) for t in trajectories]), device=self.device - ) - act = torch.as_tensor( - np.concatenate([np.array(t.act) for t in trajectories]), device=self.device - ) - rtg, adv = self._compute_rtg_and_advantage(trajectories) - - pi_loss = self._update_pi(obs, act, adv) - v_loss = 0 - for _ in range(self.train_v_iters): - v_loss = self._update_v(obs, rtg) - - return TrainEpochStats(pi_loss, v_loss) - - def _collect_trajectories(self, obs: VecEnvObs) -> TrajectoryAccumulator: - accumulator = TrajectoryAccumulator(self.env.num_envs, self.steps_per_epoch) - while not accumulator.is_done(): - action, value, _, clamped_action = self.policy.step(obs) - next_obs, reward, done, _ = self.env.step(clamped_action) - accumulator.step(obs, action, reward, done, value) - obs = next_obs - return accumulator - - def _compute_rtg_and_advantage( - self, trajectories: Sequence[Trajectory] - ) -> RtgAdvantage: - rewards_to_go = [] - advantage = [] - for traj in trajectories: - last_val = 0 if traj.terminated else self.policy.step(traj.obs[-1]).v - rew = np.append(np.array(traj.rew), last_val) - v = np.append(np.array(traj.v), last_val) - rewards_to_go.append(discounted_cumsum(rew, self.gamma)[:-1]) - deltas = rew[:-1] + self.gamma * v[1:] - v[:-1] - advantage.append(discounted_cumsum(deltas, self.gamma * self.lam)) - return RtgAdvantage( - torch.as_tensor( - np.concatenate(rewards_to_go), dtype=torch.float32, device=self.device - ), - torch.as_tensor( - np.concatenate(advantage), dtype=torch.float32, device=self.device - ), - ) - - def _update_pi( - self, obs: torch.Tensor, act: torch.Tensor, adv: torch.Tensor - ) -> float: - self.pi_optim.zero_grad() - _, logp, _ = self.policy.pi(obs, act) - pi_loss = -(logp * adv).mean() - pi_loss.backward() - nn.utils.clip_grad_norm_(self.policy.pi.parameters(), self.max_grad_norm) - self.pi_optim.step() - return pi_loss.item() - - def _update_v(self, obs: torch.Tensor, rtg: torch.Tensor) -> float: - self.val_optim.zero_grad() - v = self.policy.v(obs) - v_loss = ((v - rtg) ** 2).mean() - v_loss.backward() - nn.utils.clip_grad_norm_(self.policy.v.parameters(), self.max_grad_norm) - self.val_optim.step() - return v_loss.item() diff --git a/wrappers/atari_wrappers.py b/wrappers/atari_wrappers.py deleted file mode 100644 index f1091c1fb68fe3ab453ce72b76209834e3896ea5..0000000000000000000000000000000000000000 --- a/wrappers/atari_wrappers.py +++ /dev/null @@ -1,82 +0,0 @@ -import gym -import numpy as np - -from typing import Any, Dict, Tuple, Union - -ObsType = Union[np.ndarray, dict] -ActType = Union[int, float, np.ndarray, dict] - - -class EpisodicLifeEnv(gym.Wrapper): - def __init__(self, env: gym.Env, training: bool = True, noop_act: int = 0) -> None: - super().__init__(env) - self.training = training - self.noop_act = noop_act - self.life_done_continue = False - self.lives = 0 - - def step(self, action: ActType) -> Tuple[ObsType, float, bool, Dict[str, Any]]: - obs, rew, done, info = self.env.step(action) - new_lives = self.env.unwrapped.ale.lives() - self.life_done_continue = new_lives != self.lives and not done - # Only if training should life-end be marked as done - if self.training and 0 < new_lives < self.lives: - done = True - self.lives = new_lives - return obs, rew, done, info - - def reset(self, **kwargs) -> ObsType: - # If life_done_continue (but not game over), then a reset should just allow the - # game to progress to the next life. - if self.training and self.life_done_continue: - obs, _, _, _ = self.env.step(self.noop_act) - else: - obs = self.env.reset(**kwargs) - self.lives = self.env.unwrapped.ale.lives() - return obs - - -class FireOnLifeStarttEnv(gym.Wrapper): - def __init__(self, env: gym.Env, fire_act: int = 1) -> None: - super().__init__(env) - self.fire_act = fire_act - action_meanings = env.unwrapped.get_action_meanings() - assert action_meanings[fire_act] == "FIRE" - assert len(action_meanings) >= 3 - self.lives = 0 - self.fire_on_next_step = True - - def step(self, action: ActType) -> Tuple[ObsType, float, bool, Dict[str, Any]]: - if self.fire_on_next_step: - action = self.fire_act - self.fire_on_next_step = False - obs, rew, done, info = self.env.step(action) - new_lives = self.env.unwrapped.ale.lives() - if 0 < new_lives < self.lives and not done: - self.fire_on_next_step = True - self.lives = new_lives - return obs, rew, done, info - - def reset(self, **kwargs) -> ObsType: - self.env.reset(**kwargs) - obs, _, done, _ = self.env.step(self.fire_act) - if done: - self.env.reset(**kwargs) - obs, _, done, _ = self.env.step(2) - if done: - self.env.reset(**kwargs) - self.fire_on_next_step = False - return obs - - -class ClipRewardEnv(gym.Wrapper): - def __init__(self, env: gym.Env, training: bool = True) -> None: - super().__init__(env) - self.training = training - - def step(self, action: ActType) -> Tuple[ObsType, float, bool, Dict[str, Any]]: - obs, rew, done, info = self.env.step(action) - if self.training: - info["unclipped_reward"] = rew - rew = np.sign(rew) - return obs, rew, done, info diff --git a/wrappers/episode_record_video.py b/wrappers/episode_record_video.py deleted file mode 100644 index 7f5edd3483ae5d6c1ae29a6ce660861b0f795a30..0000000000000000000000000000000000000000 --- a/wrappers/episode_record_video.py +++ /dev/null @@ -1,73 +0,0 @@ -import gym -import numpy as np - -from gym.wrappers.monitoring.video_recorder import VideoRecorder -from typing import Tuple, Union - -ObsType = Union[np.ndarray, dict] -ActType = Union[int, float, np.ndarray, dict] - - -class EpisodeRecordVideo(gym.Wrapper): - def __init__( - self, - env: gym.Env, - video_path_prefix: str, - step_increment: int = 1, - video_step_interval: int = 1_000_000, - max_video_length: int = 3600, - ) -> None: - super().__init__(env) - self.video_path_prefix = video_path_prefix - self.step_increment = step_increment - self.video_step_interval = video_step_interval - self.max_video_length = max_video_length - self.total_steps = 0 - self.next_record_video_step = 0 - self.video_recorder = None - self.recorded_frames = 0 - - def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]: - obs, rew, done, info = self.env.step(action) - self.total_steps += self.step_increment - # Using first env to record episodes - if self.video_recorder: - self.video_recorder.capture_frame() - self.recorded_frames += 1 - if info.get("episode"): - episode_info = { - k: v.item() if hasattr(v, "item") else v - for k, v in info["episode"].items() - } - self.video_recorder.metadata["episode"] = episode_info - if self.recorded_frames > self.max_video_length: - self._close_video_recorder() - return obs, rew, done, info - - def reset(self, **kwargs) -> ObsType: - obs = self.env.reset(**kwargs) - if self.video_recorder: - self._close_video_recorder() - elif self.total_steps >= self.next_record_video_step: - self._start_video_recorder() - return obs - - def _start_video_recorder(self) -> None: - self._close_video_recorder() - - video_path = f"{self.video_path_prefix}-{self.next_record_video_step}" - self.video_recorder = VideoRecorder( - self.env, - base_path=video_path, - metadata={"step": self.total_steps}, - ) - - self.video_recorder.capture_frame() - self.recorded_frames = 1 - self.next_record_video_step += self.video_step_interval - - def _close_video_recorder(self) -> None: - if self.video_recorder: - self.video_recorder.close() - self.video_recorder = None - self.recorded_frames = 0 diff --git a/wrappers/episode_stats_writer.py b/wrappers/episode_stats_writer.py deleted file mode 100644 index d1ee1ca49bb9ac326c70bab4c7676d65f375443b..0000000000000000000000000000000000000000 --- a/wrappers/episode_stats_writer.py +++ /dev/null @@ -1,57 +0,0 @@ -import gym -import numpy as np - -from collections import deque -from stable_baselines3.common.vec_env.base_vec_env import ( - VecEnvStepReturn, - VecEnvWrapper, - VecEnvObs, -) -from torch.utils.tensorboard.writer import SummaryWriter - -from shared.stats import Episode, EpisodesStats - - -class EpisodeStatsWriter(VecEnvWrapper): - def __init__( - self, venv, tb_writer: SummaryWriter, training: bool = True, rolling_length=100 - ): - super().__init__(venv) - self.training = training - self.tb_writer = tb_writer - self.rolling_length = rolling_length - self.episodes = deque(maxlen=rolling_length) - self.total_steps = 0 - self.episode_cnt = 0 - self.last_episode_cnt_print = 0 - - def step_wait(self) -> VecEnvStepReturn: - obs, rews, dones, infos = self.venv.step_wait() - self.total_steps += self.venv.num_envs - step_episodes = [] - for info in infos: - ep_info = info.get("episode") - if ep_info: - episode = Episode(ep_info["r"], ep_info["l"]) - step_episodes.append(episode) - self.episodes.append(episode) - if step_episodes: - tag = "train" if self.training else "eval" - step_stats = EpisodesStats(step_episodes, simple=True) - step_stats.write_to_tensorboard(self.tb_writer, tag, self.total_steps) - rolling_stats = EpisodesStats(self.episodes) - rolling_stats.write_to_tensorboard( - self.tb_writer, f"{tag}_rolling", self.total_steps - ) - self.episode_cnt += len(step_episodes) - if self.episode_cnt >= self.last_episode_cnt_print + self.rolling_length: - print( - f"Episode: {self.episode_cnt} | " - f"Steps: {self.total_steps} | " - f"{rolling_stats}" - ) - self.last_episode_cnt_print += self.rolling_length - return obs, rews, dones, infos - - def reset(self) -> VecEnvObs: - return self.venv.reset() diff --git a/wrappers/initial_step_truncate_wrapper.py b/wrappers/initial_step_truncate_wrapper.py deleted file mode 100644 index 6af5a58d1ef5f9007c31c9a2b9ca67f991f81f46..0000000000000000000000000000000000000000 --- a/wrappers/initial_step_truncate_wrapper.py +++ /dev/null @@ -1,26 +0,0 @@ -import gym -import numpy as np - -from gym.wrappers.monitoring.video_recorder import VideoRecorder -from typing import Any, Dict, Tuple, Union - -ObsType = Union[np.ndarray, dict] -ActType = Union[int, float, np.ndarray, dict] - - -class InitialStepTruncateWrapper(gym.Wrapper): - def __init__(self, env: gym.Env, initial_steps_to_truncate: int) -> None: - super().__init__(env) - self.initial_steps_to_truncate = initial_steps_to_truncate - self.initialized = initial_steps_to_truncate == 0 - self.steps = 0 - - def step(self, action: ActType) -> Tuple[ObsType, float, bool, Dict[str, Any]]: - obs, rew, done, info = self.env.step(action) - if not self.initialized: - self.steps += 1 - if self.steps >= self.initial_steps_to_truncate: - print(f"Truncation at {self.steps} steps") - done = True - self.initialized = True - return obs, rew, done, info diff --git a/wrappers/no_reward_timeout.py b/wrappers/no_reward_timeout.py deleted file mode 100644 index 9ffdb838a1240f65eaf068d0c0bab2648fdfeee7..0000000000000000000000000000000000000000 --- a/wrappers/no_reward_timeout.py +++ /dev/null @@ -1,63 +0,0 @@ -import gym -import numpy as np - -from typing import Optional, Tuple, Union - -ObsType = Union[np.ndarray, dict] -ActType = Union[int, float, np.ndarray, dict] - - -class NoRewardTimeout(gym.Wrapper): - def __init__( - self, env: gym.Env, n_timeout_steps: int, n_fire_steps: Optional[int] = None - ) -> None: - super().__init__(env) - self.n_timeout_steps = n_timeout_steps - self.n_fire_steps = n_fire_steps - - self.fire_act = None - if n_fire_steps is not None: - action_meanings = env.unwrapped.get_action_meanings() - assert "FIRE" in action_meanings - self.fire_act = action_meanings.index("FIRE") - - self.steps_since_reward = 0 - - self.episode_score = 0 - self.episode_step_idx = 0 - - def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]: - if self.steps_since_reward == self.n_fire_steps: - assert self.fire_act is not None - self.print_intervention("Force fire action") - action = self.fire_act - obs, rew, done, info = self.env.step(action) - - self.episode_score += rew - self.episode_step_idx += 1 - - if rew != 0 or done: - self.steps_since_reward = 0 - else: - self.steps_since_reward += 1 - if self.steps_since_reward >= self.n_timeout_steps: - self.print_intervention("Early terminate") - done = True - - return obs, rew, done, info - - def reset(self, **kwargs) -> ObsType: - self._reset_state() - return self.env.reset(**kwargs) - - def _reset_state(self) -> None: - self.steps_since_reward = 0 - self.episode_score = 0 - self.episode_step_idx = 0 - - def print_intervention(self, tag: str) -> None: - print( - f"{self.__class__.__name__}: {tag} | " - f"Score: {self.episode_score} | " - f"Length: {self.episode_step_idx}" - ) diff --git a/wrappers/vec_episode_recorder.py b/wrappers/vec_episode_recorder.py deleted file mode 100644 index f8b3e59a9c89313a34b42ee62210020902d4132f..0000000000000000000000000000000000000000 --- a/wrappers/vec_episode_recorder.py +++ /dev/null @@ -1,56 +0,0 @@ -import numpy as np - -from gym.wrappers.monitoring.video_recorder import VideoRecorder -from stable_baselines3.common.vec_env.base_vec_env import ( - VecEnv, - VecEnvStepReturn, - VecEnvWrapper, - VecEnvObs, -) -from typing import Optional - - -class VecEpisodeRecorder(VecEnvWrapper): - def __init__(self, venv: VecEnv, base_path: str, max_video_length: int = 3600): - super().__init__(venv) - self.base_path = base_path - self.max_video_length = max_video_length - self.video_recorder = None - self.recorded_frames = 0 - - def step_wait(self) -> VecEnvStepReturn: - obs, rew, dones, infos = self.venv.step_wait() - # Using first env to record episodes - if self.video_recorder: - self.video_recorder.capture_frame() - self.recorded_frames += 1 - if dones[0] and infos[0].get("episode"): - episode_info = { - k: v.item() if hasattr(v, "item") else v - for k, v in infos[0]["episode"].items() - } - self.video_recorder.metadata["episode"] = episode_info - if dones[0] or self.recorded_frames > self.max_video_length: - self._close_video_recorder() - return obs, rew, dones, infos - - def reset(self) -> VecEnvObs: - obs = self.venv.reset() - self._start_video_recorder() - return obs - - def _start_video_recorder(self) -> None: - self._close_video_recorder() - - self.video_recorder = VideoRecorder( - self.venv, - base_path=self.base_path, - ) - - self.video_recorder.capture_frame() - self.recorded_frames = 1 - - def _close_video_recorder(self) -> None: - if self.video_recorder: - self.video_recorder.close() - self.video_recorder = None diff --git a/wrappers/vec_single_recorder.py b/wrappers/vec_single_recorder.py deleted file mode 100644 index effe3019a43a25d3ce1b8458eb4409112a04ee2f..0000000000000000000000000000000000000000 --- a/wrappers/vec_single_recorder.py +++ /dev/null @@ -1,92 +0,0 @@ -import numpy as np - -from gym.wrappers.monitoring.video_recorder import VideoRecorder -from stable_baselines3.common.vec_env.base_vec_env import ( - VecEnv, - VecEnvStepReturn, - VecEnvWrapper, - VecEnvObs, -) -from typing import Optional - - -class VecSingleImageWrapper(VecEnvWrapper): - def __init__(self, venv: VecEnv): - super().__init__(venv) - - def render(self, mode: str = "human") -> Optional[np.ndarray]: - images = self.venv.get_images() - return images[0] - - def step_wait(self) -> VecEnvStepReturn: - return self.venv.step_wait() - - def reset(self) -> VecEnvObs: - return self.venv.reset() - - -class VecSingleRecorder(VecEnvWrapper): - def __init__( - self, - venv: VecEnv, - video_path_prefix: str, - video_step_interval: int = 1_000_000, - max_video_length: int = 3600, - ): - super().__init__(venv) - self.single_image_wrapper = VecSingleImageWrapper(venv) - self.video_path_prefix = video_path_prefix - self.video_step_interval = video_step_interval - self.max_video_length = max_video_length - self.total_steps = 0 - self.next_record_video_step = 0 - self.video_recorder = None - self.recorded_frames = 0 - - def step_wait(self) -> VecEnvStepReturn: - obs, rew, dones, infos = self.venv.step_wait() - self.total_steps += self.venv.num_envs - # Using first env to record episodes - if self.video_recorder: - self.video_recorder.capture_frame() - self.recorded_frames += 1 - if dones[0] and infos[0].get("episode"): - episode_info = { - k: v.item() if hasattr(v, "item") else v - for k, v in infos[0]["episode"].items() - } - self.video_recorder.metadata["episode"] = episode_info - if dones[0] or self.recorded_frames > self.max_video_length: - self._close_video_recorder() - elif dones[0] and self.total_steps >= self.next_record_video_step: - self._start_video_recorder() - return obs, rew, dones, infos - - def reset(self) -> VecEnvObs: - obs = self.venv.reset() - if self.video_recorder: - self._close_video_recorder() - elif ( - not self.video_recorder and self.total_steps >= self.next_record_video_step - ): - self._start_video_recorder() - return obs - - def _start_video_recorder(self) -> None: - self._close_video_recorder() - - video_path = f"{self.video_path_prefix}-{self.next_record_video_step}" - self.video_recorder = VideoRecorder( - self.single_image_wrapper, - base_path=video_path, - metadata={"step": self.total_steps}, - ) - - self.video_recorder.capture_frame() - self.recorded_frames = 1 - self.next_record_video_step += self.video_step_interval - - def _close_video_recorder(self) -> None: - if self.video_recorder: - self.video_recorder.close() - self.video_recorder = None diff --git a/wrappers/video_compat_wrapper.py b/wrappers/video_compat_wrapper.py deleted file mode 100644 index c75cbd43976209f4e47eceddc0b94504ed19b584..0000000000000000000000000000000000000000 --- a/wrappers/video_compat_wrapper.py +++ /dev/null @@ -1,13 +0,0 @@ -import gym -import numpy as np - - -class VideoCompatWrapper(gym.Wrapper): - def __init__(self, env: gym.Env) -> None: - super().__init__(env) - - def render(self, mode="human", **kwargs): - r = super().render(mode=mode, **kwargs) - if mode == "rgb_array" and isinstance(r, np.ndarray) and r.dtype != np.uint8: - r = r.astype(np.uint8) - return r