File size: 6,529 Bytes
b3d7810 ec6152b b3d7810 ec6152b b3d7810 ec6152b b3d7810 ec6152b b3d7810 ec6152b b3d7810 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 |
import copy
import logging
import random
from collections import deque
from typing import List, NamedTuple, Optional, TypeVar
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam
from torch.utils.tensorboard.writer import SummaryWriter
from rl_algo_impls.dqn.policy import DQNPolicy
from rl_algo_impls.shared.algorithm import Algorithm
from rl_algo_impls.shared.callbacks import Callback
from rl_algo_impls.shared.schedule import linear_schedule
from rl_algo_impls.wrappers.vectorable_wrapper import VecEnv, VecEnvObs
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, callbacks: Optional[List[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 callbacks:
if not all(
c.on_step(timesteps_elapsed=rollout_steps) for c in callbacks
):
logging.info(
f"Callback terminated training at {timesteps_elapsed} timesteps"
)
break
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
)
|