pushing model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+ppo_atari_envpool_async_jax_scan_impalanet_machado.cleanrl_model filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,87 @@
+---
+tags:
+- WizardOfWor-v5
+- deep-reinforcement-learning
+- reinforcement-learning
+- custom-implementation
+library_name: cleanrl
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: WizardOfWor-v5
+      type: WizardOfWor-v5
+    metrics:
+    - type: mean_reward
+      value: 5120.00 +/- 2845.28
+      name: mean_reward
+      verified: false
+---
+
+# (CleanRL) **PPO** Agent Playing **WizardOfWor-v5**
+
+This is a trained model of a PPO agent playing WizardOfWor-v5.
+The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
+found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py).
+
+## Get Started
+
+To use this model, please install the `cleanrl` package with the following command:
+
+```
+pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
+python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id WizardOfWor-v5
+```
+
+Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
+
+
+## Command to reproduce the training
+
+```bash
+curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
+curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
+curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
+poetry install --all-extras
+python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id WizardOfWor-v5 --seed 1
+```
+
+# Hyperparameters
+```python
+{'anneal_lr': True,
+ 'async_batch_size': 16,
+ 'batch_size': 2048,
+ 'capture_video': False,
+ 'clip_coef': 0.1,
+ 'cuda': True,
+ 'ent_coef': 0.01,
+ 'env_id': 'WizardOfWor-v5',
+ 'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
+ 'gae': True,
+ 'gae_lambda': 0.95,
+ 'gamma': 0.99,
+ 'hf_entity': 'cleanrl',
+ 'learning_rate': 0.00025,
+ 'max_grad_norm': 0.5,
+ 'minibatch_size': 1024,
+ 'norm_adv': True,
+ 'num_envs': 64,
+ 'num_minibatches': 2,
+ 'num_steps': 32,
+ 'num_updates': 24414,
+ 'save_model': True,
+ 'seed': 1,
+ 'target_kl': None,
+ 'torch_deterministic': True,
+ 'total_timesteps': 50000000,
+ 'track': True,
+ 'update_epochs': 2,
+ 'upload_model': True,
+ 'vf_coef': 0.5,
+ 'wandb_entity': None,
+ 'wandb_project_name': 'envpool-atari'}
+```
+    

events.out.tfevents.1673478005.pop-os.3960872.0

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ppo_atari_envpool_async_jax_scan_impalanet_machado.cleanrl_model

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ppo_atari_envpool_async_jax_scan_impalanet_machado.py

@@ -0,0 +1,626 @@
+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_envpool_async_jax_scan_impalanet_machadopy
+# https://gregorygundersen.com/blog/2020/02/09/log-sum-exp/
+import argparse
+import os
+import random
+import time
+from distutils.util import strtobool
+from typing import Sequence
+
+os.environ[
+    "XLA_PYTHON_CLIENT_MEM_FRACTION"
+] = "0.7"  # see https://github.com/google/jax/discussions/6332#discussioncomment-1279991
+
+import envpool
+import flax
+import flax.linen as nn
+import gym
+import jax
+import jax.numpy as jnp
+import numpy as np
+import optax
+from flax.linen.initializers import constant, orthogonal
+from flax.training.train_state import TrainState
+from torch.utils.tensorboard import SummaryWriter
+
+
+def parse_args():
+    # fmt: off
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help="the name of this experiment")
+    parser.add_argument("--seed", type=int, default=1,
+        help="seed of the experiment")
+    parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="if toggled, `torch.backends.cudnn.deterministic=False`")
+    parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="if toggled, cuda will be enabled by default")
+    parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="if toggled, this experiment will be tracked with Weights and Biases")
+    parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument("--wandb-entity", type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="weather to capture videos of the agent performances (check out `videos` folder)")
+    parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to save model into the `runs/{run_name}` folder")
+    parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to upload the saved model to huggingface")
+    parser.add_argument("--hf-entity", type=str, default="",
+        help="the user or org name of the model repository from the Hugging Face Hub")
+
+    # Algorithm specific arguments
+    parser.add_argument("--env-id", type=str, default="Breakout-v5",
+        help="the id of the environment")
+    parser.add_argument("--total-timesteps", type=int, default=50000000,
+        help="total timesteps of the experiments")
+    parser.add_argument("--learning-rate", type=float, default=2.5e-4,
+        help="the learning rate of the optimizer")
+    parser.add_argument("--num-envs", type=int, default=64,
+        help="the number of parallel game environments")
+    parser.add_argument("--async-batch-size", type=int, default=16,
+        help="the envpool's batch size in the async mode")
+    parser.add_argument("--num-steps", type=int, default=32,
+        help="the number of steps to run in each environment per policy rollout")
+    parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="Toggle learning rate annealing for policy and value networks")
+    parser.add_argument("--gae", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="Use GAE for advantage computation")
+    parser.add_argument("--gamma", type=float, default=0.99,
+        help="the discount factor gamma")
+    parser.add_argument("--gae-lambda", type=float, default=0.95,
+        help="the lambda for the general advantage estimation")
+    parser.add_argument("--num-minibatches", type=int, default=2,
+        help="the number of mini-batches")
+    parser.add_argument("--update-epochs", type=int, default=2,
+        help="the K epochs to update the policy")
+    parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="Toggles advantages normalization")
+    parser.add_argument("--clip-coef", type=float, default=0.1,
+        help="the surrogate clipping coefficient")
+    parser.add_argument("--ent-coef", type=float, default=0.01,
+        help="coefficient of the entropy")
+    parser.add_argument("--vf-coef", type=float, default=0.5,
+        help="coefficient of the value function")
+    parser.add_argument("--max-grad-norm", type=float, default=0.5,
+        help="the maximum norm for the gradient clipping")
+    parser.add_argument("--target-kl", type=float, default=None,
+        help="the target KL divergence threshold")
+    args = parser.parse_args()
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    args.num_updates = args.total_timesteps // args.batch_size
+    # fmt: on
+    return args
+
+
+def make_env(env_id, seed, num_envs, async_batch_size=1):
+    def thunk():
+        envs = envpool.make(
+            env_id,
+            env_type="gym",
+            num_envs=num_envs,
+            batch_size=async_batch_size,
+            episodic_life=False,  # Machado et al. 2017 (Revisitng ALE: Eval protocols) p. 6
+            repeat_action_probability=0.25,  # Machado et al. 2017 (Revisitng ALE: Eval protocols) p. 12
+            noop_max=1,  # Machado et al. 2017 (Revisitng ALE: Eval protocols) p. 12 (no-op is deprecated in favor of sticky action, right?)
+            full_action_space=True,  # Machado et al. 2017 (Revisitng ALE: Eval protocols) Tab. 5
+            max_episode_steps=int(108000 / 4),  # Hessel et al. 2018 (Rainbow DQN), Table 3, Max frames per episode
+            reward_clip=True,
+            seed=seed,
+        )
+        envs.num_envs = num_envs
+        envs.single_action_space = envs.action_space
+        envs.single_observation_space = envs.observation_space
+        envs.is_vector_env = True
+        return envs
+
+    return thunk
+
+
+class ResidualBlock(nn.Module):
+    channels: int
+
+    @nn.compact
+    def __call__(self, x):
+        inputs = x
+        x = nn.relu(x)
+        x = nn.Conv(
+            self.channels,
+            kernel_size=(3, 3),
+        )(x)
+        x = nn.relu(x)
+        x = nn.Conv(
+            self.channels,
+            kernel_size=(3, 3),
+        )(x)
+        return x + inputs
+
+
+class ConvSequence(nn.Module):
+    channels: int
+
+    @nn.compact
+    def __call__(self, x):
+        x = nn.Conv(
+            self.channels,
+            kernel_size=(3, 3),
+        )(x)
+        x = nn.max_pool(x, window_shape=(3, 3), strides=(2, 2), padding="SAME")
+        x = ResidualBlock(self.channels)(x)
+        x = ResidualBlock(self.channels)(x)
+        return x
+
+
+class Network(nn.Module):
+    channelss: Sequence[int] = (16, 32, 32)
+
+    @nn.compact
+    def __call__(self, x):
+        x = jnp.transpose(x, (0, 2, 3, 1))
+        x = x / (255.0)
+        for channels in self.channelss:
+            x = ConvSequence(channels)(x)
+        x = nn.relu(x)
+        x = x.reshape((x.shape[0], -1))
+        x = nn.Dense(256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(x)
+        x = nn.relu(x)
+        return x
+
+
+class Critic(nn.Module):
+    @nn.compact
+    def __call__(self, x):
+        return nn.Dense(1, kernel_init=orthogonal(1), bias_init=constant(0.0))(x)
+
+
+class Actor(nn.Module):
+    action_dim: Sequence[int]
+
+    @nn.compact
+    def __call__(self, x):
+        return nn.Dense(self.action_dim, kernel_init=orthogonal(0.01), bias_init=constant(0.0))(x)
+
+
+@flax.struct.dataclass
+class AgentParams:
+    network_params: flax.core.FrozenDict
+    actor_params: flax.core.FrozenDict
+    critic_params: flax.core.FrozenDict
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer = SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
+    )
+
+    # TRY NOT TO MODIFY: seeding
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    key = jax.random.PRNGKey(args.seed)
+    key, network_key, actor_key, critic_key = jax.random.split(key, 4)
+
+    # env setup
+    envs = make_env(args.env_id, args.seed, args.num_envs, args.async_batch_size)()
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    def linear_schedule(count):
+        # anneal learning rate linearly after one training iteration which contains
+        # (args.num_minibatches * args.update_epochs) gradient updates
+        frac = 1.0 - (count // (args.num_minibatches * args.update_epochs)) / args.num_updates
+        return args.learning_rate * frac
+
+    network = Network()
+    actor = Actor(action_dim=envs.single_action_space.n)
+    critic = Critic()
+    network_params = network.init(network_key, np.array([envs.single_observation_space.sample()]))
+    agent_state = TrainState.create(
+        apply_fn=None,
+        params=AgentParams(
+            network_params,
+            actor.init(actor_key, network.apply(network_params, np.array([envs.single_observation_space.sample()]))),
+            critic.init(critic_key, network.apply(network_params, np.array([envs.single_observation_space.sample()]))),
+        ),
+        tx=optax.chain(
+            optax.clip_by_global_norm(args.max_grad_norm),
+            optax.inject_hyperparams(optax.adam)(
+                learning_rate=linear_schedule if args.anneal_lr else args.learning_rate, eps=1e-5
+            ),
+        ),
+    )
+
+    @jax.jit
+    def get_action_and_value(
+        agent_state: TrainState,
+        next_obs: np.ndarray,
+        key: jax.random.PRNGKey,
+    ):
+        hidden = network.apply(agent_state.params.network_params, next_obs)
+        logits = actor.apply(agent_state.params.actor_params, hidden)
+        # sample action: Gumbel-softmax trick
+        # see https://stats.stackexchange.com/questions/359442/sampling-from-a-categorical-distribution
+        key, subkey = jax.random.split(key)
+        u = jax.random.uniform(subkey, shape=logits.shape)
+        action = jnp.argmax(logits - jnp.log(-jnp.log(u)), axis=1)
+        logprob = jax.nn.log_softmax(logits)[jnp.arange(action.shape[0]), action]
+        value = critic.apply(agent_state.params.critic_params, hidden)
+        return action, logprob, value.squeeze(), key
+
+    @jax.jit
+    def get_action_and_value2(
+        params: flax.core.FrozenDict,
+        x: np.ndarray,
+        action: np.ndarray,
+    ):
+        hidden = network.apply(params.network_params, x)
+        logits = actor.apply(params.actor_params, hidden)
+        logprob = jax.nn.log_softmax(logits)[jnp.arange(action.shape[0]), action]
+        logits = logits - jax.scipy.special.logsumexp(logits, axis=-1, keepdims=True)
+        logits = logits.clip(min=jnp.finfo(logits.dtype).min)
+        p_log_p = logits * jax.nn.softmax(logits)
+        entropy = -p_log_p.sum(-1)
+        value = critic.apply(params.critic_params, hidden).squeeze()
+        return logprob, entropy, value
+
+    def compute_gae_once(carry, x):
+        lastvalues, lastdones, advantages, lastgaelam, final_env_ids, final_env_id_checked = carry
+        (
+            done,
+            value,
+            eid,
+            reward,
+        ) = x
+        nextnonterminal = 1.0 - lastdones[eid]
+        nextvalues = lastvalues[eid]
+        delta = jnp.where(final_env_id_checked[eid] == -1, 0, reward + args.gamma * nextvalues * nextnonterminal - value)
+        advantages = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam[eid]
+        final_env_ids = jnp.where(final_env_id_checked[eid] == 1, 1, 0)
+        final_env_id_checked = final_env_id_checked.at[eid].set(
+            jnp.where(final_env_id_checked[eid] == -1, 1, final_env_id_checked[eid])
+        )
+
+        # the last_ variables keeps track of the actual `num_steps`
+        lastgaelam = lastgaelam.at[eid].set(advantages)
+        lastdones = lastdones.at[eid].set(done)
+        lastvalues = lastvalues.at[eid].set(value)
+        return (lastvalues, lastdones, advantages, lastgaelam, final_env_ids, final_env_id_checked), (
+            advantages,
+            final_env_ids,
+        )
+
+    @jax.jit
+    def compute_gae(
+        env_ids: np.ndarray,
+        rewards: np.ndarray,
+        values: np.ndarray,
+        dones: np.ndarray,
+    ):
+        dones = jnp.asarray(dones)
+        values = jnp.asarray(values)
+        env_ids = jnp.asarray(env_ids)
+        rewards = jnp.asarray(rewards)
+
+        _, B = env_ids.shape
+        final_env_id_checked = jnp.zeros(args.num_envs, jnp.int32) - 1
+        final_env_ids = jnp.zeros(B, jnp.int32)
+        advantages = jnp.zeros(B)
+        lastgaelam = jnp.zeros(args.num_envs)
+        lastdones = jnp.zeros(args.num_envs) + 1
+        lastvalues = jnp.zeros(args.num_envs)
+
+        (_, _, _, _, final_env_ids, final_env_id_checked), (advantages, final_env_ids) = jax.lax.scan(
+            compute_gae_once,
+            (
+                lastvalues,
+                lastdones,
+                advantages,
+                lastgaelam,
+                final_env_ids,
+                final_env_id_checked,
+            ),
+            (
+                dones,
+                values,
+                env_ids,
+                rewards,
+            ),
+            reverse=True,
+        )
+        return advantages, advantages + values, final_env_id_checked, final_env_ids
+
+    def ppo_loss(params, x, a, logp, mb_advantages, mb_returns):
+        newlogprob, entropy, newvalue = get_action_and_value2(params, x, a)
+        logratio = newlogprob - logp
+        ratio = jnp.exp(logratio)
+        approx_kl = ((ratio - 1) - logratio).mean()
+
+        if args.norm_adv:
+            mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
+
+        # Policy loss
+        pg_loss1 = -mb_advantages * ratio
+        pg_loss2 = -mb_advantages * jnp.clip(ratio, 1 - args.clip_coef, 1 + args.clip_coef)
+        pg_loss = jnp.maximum(pg_loss1, pg_loss2).mean()
+
+        # Value loss
+        v_loss = 0.5 * ((newvalue - mb_returns) ** 2).mean()
+
+        entropy_loss = entropy.mean()
+        loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+        return loss, (pg_loss, v_loss, entropy_loss, jax.lax.stop_gradient(approx_kl))
+
+    ppo_loss_grad_fn = jax.value_and_grad(ppo_loss, has_aux=True)
+
+    @jax.jit
+    def update_ppo(
+        agent_state: TrainState,
+        obs: list,
+        dones: list,
+        values: list,
+        actions: list,
+        logprobs: list,
+        env_ids: list,
+        rewards: list,
+        key: jax.random.PRNGKey,
+    ):
+        obs = jnp.asarray(obs)
+        dones = jnp.asarray(dones)
+        values = jnp.asarray(values)
+        actions = jnp.asarray(actions)
+        logprobs = jnp.asarray(logprobs)
+        env_ids = jnp.asarray(env_ids)
+        rewards = jnp.asarray(rewards)
+
+        # TODO: in an unlikely event, one of the envs might have not stepped at all, which may results in unexpected behavior
+        T, B = env_ids.shape
+        index_ranges = jnp.arange(T * B, dtype=jnp.int32)
+        next_index_ranges = jnp.zeros_like(index_ranges, dtype=jnp.int32)
+        last_env_ids = jnp.zeros(args.num_envs, dtype=jnp.int32) - 1
+
+        def f(carry, x):
+            last_env_ids, next_index_ranges = carry
+            env_id, index_range = x
+            next_index_ranges = next_index_ranges.at[last_env_ids[env_id]].set(
+                jnp.where(last_env_ids[env_id] != -1, index_range, next_index_ranges[last_env_ids[env_id]])
+            )
+            last_env_ids = last_env_ids.at[env_id].set(index_range)
+            return (last_env_ids, next_index_ranges), None
+
+        (last_env_ids, next_index_ranges), _ = jax.lax.scan(
+            f,
+            (last_env_ids, next_index_ranges),
+            (env_ids.reshape(-1), index_ranges),
+        )
+
+        # rewards is off by one time step
+        rewards = rewards.reshape(-1)[next_index_ranges].reshape((args.num_steps) * async_update, args.async_batch_size)
+        advantages, returns, _, final_env_ids = compute_gae(env_ids, rewards, values, dones)
+        b_inds = jnp.nonzero(final_env_ids.reshape(-1), size=(args.num_steps) * async_update * args.async_batch_size)[0]
+        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+        b_actions = actions.reshape(-1)
+        b_logprobs = logprobs.reshape(-1)
+        b_advantages = advantages.reshape(-1)
+        b_returns = returns.reshape(-1)
+
+        def update_epoch(carry, _):
+            agent_state, key = carry
+            key, subkey = jax.random.split(key)
+
+            # taken from: https://github.com/google/brax/blob/main/brax/training/agents/ppo/train.py
+            def convert_data(x: jnp.ndarray):
+                x = jax.random.permutation(subkey, x)
+                x = jnp.reshape(x, (args.num_minibatches, -1) + x.shape[1:])
+                return x
+
+            def update_minibatch(agent_state, minibatch):
+                mb_obs, mb_actions, mb_logprobs, mb_advantages, mb_returns = minibatch
+                (loss, (pg_loss, v_loss, entropy_loss, approx_kl)), grads = ppo_loss_grad_fn(
+                    agent_state.params,
+                    mb_obs,
+                    mb_actions,
+                    mb_logprobs,
+                    mb_advantages,
+                    mb_returns,
+                )
+                agent_state = agent_state.apply_gradients(grads=grads)
+                return agent_state, (loss, pg_loss, v_loss, entropy_loss, approx_kl, grads)
+
+            agent_state, (loss, pg_loss, v_loss, entropy_loss, approx_kl, grads) = jax.lax.scan(
+                update_minibatch,
+                agent_state,
+                (
+                    convert_data(b_obs),
+                    convert_data(b_actions),
+                    convert_data(b_logprobs),
+                    convert_data(b_advantages),
+                    convert_data(b_returns),
+                ),
+            )
+            return (agent_state, key), (loss, pg_loss, v_loss, entropy_loss, approx_kl, grads)
+
+        (agent_state, key), (loss, pg_loss, v_loss, entropy_loss, approx_kl, _) = jax.lax.scan(
+            update_epoch, (agent_state, key), (), length=args.update_epochs
+        )
+        return agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, advantages, returns, b_inds, final_env_ids, key
+
+    # TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time = time.time()
+    async_update = int(args.num_envs / args.async_batch_size)
+
+    # put data in the last index
+    episode_returns = np.zeros((args.num_envs,), dtype=np.float32)
+    returned_episode_returns = np.zeros((args.num_envs,), dtype=np.float32)
+    episode_lengths = np.zeros((args.num_envs,), dtype=np.float32)
+    returned_episode_lengths = np.zeros((args.num_envs,), dtype=np.float32)
+    envs.async_reset()
+    final_env_ids = np.zeros((async_update, args.async_batch_size), dtype=np.int32)
+
+    for update in range(1, args.num_updates + 2):
+        update_time_start = time.time()
+        obs = []
+        dones = []
+        actions = []
+        logprobs = []
+        values = []
+        env_ids = []
+        rewards = []
+        truncations = []
+        terminations = []
+        env_recv_time = 0
+        inference_time = 0
+        storage_time = 0
+        env_send_time = 0
+
+        # NOTE: This is a major difference from the sync version:
+        # at the end of the rollout phase, the sync version will have the next observation
+        # ready for the value bootstrap, but the async version will not have it.
+        # for this reason we do `num_steps + 1`` to get the extra states for value bootstrapping.
+        # but note that the extra states are not used for the loss computation in the next iteration,
+        # while the sync version will use the extra state for the loss computation.
+        for step in range(
+            async_update, (args.num_steps + 1) * async_update
+        ):  # num_steps + 1 to get the states for value bootstrapping.
+            env_recv_time_start = time.time()
+            next_obs, next_reward, next_done, info = envs.recv()
+            env_recv_time += time.time() - env_recv_time_start
+            global_step += len(next_done)
+            env_id = info["env_id"]
+
+            inference_time_start = time.time()
+            action, logprob, value, key = get_action_and_value(agent_state, next_obs, key)
+            inference_time += time.time() - inference_time_start
+
+            env_send_time_start = time.time()
+            envs.send(np.array(action), env_id)
+            env_send_time += time.time() - env_send_time_start
+            storage_time_start = time.time()
+            obs.append(next_obs)
+            dones.append(next_done)
+            values.append(value)
+            actions.append(action)
+            logprobs.append(logprob)
+            env_ids.append(env_id)
+            rewards.append(next_reward)
+            truncations.append(info["TimeLimit.truncated"])
+            terminations.append(info["terminated"])
+            episode_returns[env_id] += info["reward"]
+            returned_episode_returns[env_id] = np.where(
+                info["terminated"] + info["TimeLimit.truncated"], episode_returns[env_id], returned_episode_returns[env_id]
+            )
+            episode_returns[env_id] *= (1 - info["terminated"]) * (1 - info["TimeLimit.truncated"])
+            episode_lengths[env_id] += 1
+            returned_episode_lengths[env_id] = np.where(
+                info["terminated"] + info["TimeLimit.truncated"], episode_lengths[env_id], returned_episode_lengths[env_id]
+            )
+            episode_lengths[env_id] *= (1 - info["terminated"]) * (1 - info["TimeLimit.truncated"])
+            storage_time += time.time() - storage_time_start
+
+        avg_episodic_return = np.mean(returned_episode_returns)
+        # print(returned_episode_returns)
+        print(f"global_step={global_step}, avg_episodic_return={avg_episodic_return}")
+        writer.add_scalar("charts/avg_episodic_return", avg_episodic_return, global_step)
+        writer.add_scalar("charts/avg_episodic_length", np.mean(returned_episode_lengths), global_step)
+        training_time_start = time.time()
+        (
+            agent_state,
+            loss,
+            pg_loss,
+            v_loss,
+            entropy_loss,
+            approx_kl,
+            advantages,
+            returns,
+            b_inds,
+            final_env_ids,
+            key,
+        ) = update_ppo(
+            agent_state,
+            obs,
+            dones,
+            values,
+            actions,
+            logprobs,
+            env_ids,
+            rewards,
+            key,
+        )
+        writer.add_scalar("stats/training_time", time.time() - training_time_start, global_step)
+        # writer.add_scalar("stats/advantages", advantages.mean().item(), global_step)
+        # writer.add_scalar("stats/returns", returns.mean().item(), global_step)
+        writer.add_scalar("stats/truncations", np.sum(truncations), global_step)
+        writer.add_scalar("stats/terminations", np.sum(terminations), global_step)
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/learning_rate", agent_state.opt_state[1].hyperparams["learning_rate"].item(), global_step)
+        writer.add_scalar("losses/value_loss", v_loss[-1, -1].item(), global_step)
+        writer.add_scalar("losses/policy_loss", pg_loss[-1, -1].item(), global_step)
+        writer.add_scalar("losses/entropy", entropy_loss[-1, -1].item(), global_step)
+        writer.add_scalar("losses/approx_kl", approx_kl[-1, -1].item(), global_step)
+        writer.add_scalar("losses/loss", loss[-1, -1].item(), global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
+        writer.add_scalar(
+            "charts/SPS_update", int(args.num_envs * args.num_steps / (time.time() - update_time_start)), global_step
+        )
+        writer.add_scalar("stats/env_recv_time", env_recv_time, global_step)
+        writer.add_scalar("stats/inference_time", inference_time, global_step)
+        writer.add_scalar("stats/storage_time", storage_time, global_step)
+        writer.add_scalar("stats/env_send_time", env_send_time, global_step)
+        writer.add_scalar("stats/update_time", time.time() - update_time_start, global_step)
+
+    if args.save_model:
+        model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
+        with open(model_path, "wb") as f:
+            f.write(
+                flax.serialization.to_bytes(
+                    [
+                        vars(args),
+                        [
+                            agent_state.params.network_params,
+                            agent_state.params.actor_params,
+                            agent_state.params.critic_params,
+                        ],
+                    ]
+                )
+            )
+        print(f"model saved to {model_path}")
+        from cleanrl_utils.evals.ppo_envpool_jax_eval import evaluate
+
+        episodic_returns = evaluate(
+            model_path,
+            make_env,
+            args.env_id,
+            eval_episodes=10,
+            run_name=f"{run_name}-eval",
+            Model=(Network, Actor, Critic),
+        )
+        for idx, episodic_return in enumerate(episodic_returns):
+            writer.add_scalar("eval/episodic_return", episodic_return, idx)
+
+        if args.upload_model:
+            from cleanrl_utils.huggingface import push_to_hub
+
+            repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
+            repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
+            push_to_hub(args, episodic_returns, repo_id, "PPO", f"runs/{run_name}", f"videos/{run_name}-eval")
+
+    envs.close()
+    writer.close()

pyproject.toml

@@ -0,0 +1,178 @@
+[tool.poetry]
+name = "cleanrl-test"
+version = "1.1.2"
+description = "High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features"
+authors = ["Costa Huang <[email protected]>"]
+packages = [
+    { include = "cleanrl" },
+    { include = "cleanrl_utils" },
+]
+keywords = ["reinforcement", "machine", "learning", "research"]
+license="MIT"
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.7.1,<3.10"
+tensorboard = "^2.10.0"
+wandb = "^0.13.6"
+gym = "0.23.1"
+torch = ">=1.12.1"
+stable-baselines3 = "1.2.0"
+gymnasium = "^0.26.3"
+moviepy = "^1.0.3"
+pygame = "2.1.0"
+huggingface-hub = "^0.11.1"
+
+ale-py = {version = "0.7.4", optional = true}
+AutoROM = {extras = ["accept-rom-license"], version = "^0.4.2"}
+opencv-python = {version = "^4.6.0.66", optional = true}
+pybullet = {version = "3.1.8", optional = true}
+procgen = {version = "^0.10.7", optional = true}
+pytest = {version = "^7.1.3", optional = true}
+mujoco = {version = "^2.2", optional = true}
+imageio = {version = "^2.14.1", optional = true}
+free-mujoco-py = {version = "^2.1.6", optional = true}
+mkdocs-material = {version = "^8.4.3", optional = true}
+markdown-include = {version = "^0.7.0", optional = true}
+jax = {version = "^0.3.17", optional = true}
+jaxlib = {version = "^0.3.15", optional = true}
+flax = {version = "^0.6.0", optional = true}
+optuna = {version = "^3.0.1", optional = true}
+optuna-dashboard = {version = "^0.7.2", optional = true}
+rich = {version = "<12.0", optional = true}
+envpool = {version = "^0.7.1", optional = true}
+PettingZoo = {version = "1.18.1", optional = true}
+SuperSuit = {version = "3.4.0", optional = true}
+multi-agent-ale-py = {version = "0.1.11", optional = true}
+boto3 = {version = "^1.24.70", optional = true}
+awscli = {version = "^1.25.71", optional = true}
+shimmy = {version = "^0.1.0", optional = true}
+dm-control = {version = "^1.0.8", optional = true}
+
+[tool.poetry.group.dev.dependencies]
+pre-commit = "^2.20.0"
+
+[tool.poetry.group.atari]
+optional = true
+[tool.poetry.group.atari.dependencies]
+ale-py = "0.7.4"
+AutoROM = {extras = ["accept-rom-license"], version = "^0.4.2"}
+opencv-python = "^4.6.0.66"
+
+[tool.poetry.group.pybullet]
+optional = true
+[tool.poetry.group.pybullet.dependencies]
+pybullet = "3.1.8"
+
+[tool.poetry.group.procgen]
+optional = true
+[tool.poetry.group.procgen.dependencies]
+procgen = "^0.10.7"
+
+[tool.poetry.group.pytest]
+optional = true
+[tool.poetry.group.pytest.dependencies]
+pytest = "^7.1.3"
+
+[tool.poetry.group.mujoco]
+optional = true
+[tool.poetry.group.mujoco.dependencies]
+mujoco = "^2.2"
+imageio = "^2.14.1"
+
+[tool.poetry.group.mujoco_py]
+optional = true
+[tool.poetry.group.mujoco_py.dependencies]
+free-mujoco-py = "^2.1.6"
+
+[tool.poetry.group.docs]
+optional = true
+[tool.poetry.group.docs.dependencies]
+mkdocs-material = "^8.4.3"
+markdown-include = "^0.7.0"
+
+[tool.poetry.group.jax]
+optional = true
+[tool.poetry.group.jax.dependencies]
+jax = "^0.3.17"
+jaxlib = "^0.3.15"
+flax = "^0.6.0"
+
+[tool.poetry.group.optuna]
+optional = true
+[tool.poetry.group.optuna.dependencies]
+optuna = "^3.0.1"
+optuna-dashboard = "^0.7.2"
+rich = "<12.0"
+
+[tool.poetry.group.envpool]
+optional = true
+[tool.poetry.group.envpool.dependencies]
+envpool = "^0.7.1"
+
+[tool.poetry.group.pettingzoo]
+optional = true
+[tool.poetry.group.pettingzoo.dependencies]
+PettingZoo = "1.18.1"
+SuperSuit = "3.4.0"
+multi-agent-ale-py = "0.1.11"
+
+[tool.poetry.group.cloud]
+optional = true
+[tool.poetry.group.cloud.dependencies]
+boto3 = "^1.24.70"
+awscli = "^1.25.71"
+
+[tool.poetry.group.isaacgym]
+optional = true
+[tool.poetry.group.isaacgym.dependencies]
+isaacgymenvs = {git = "https://github.com/vwxyzjn/IsaacGymEnvs.git", rev = "poetry"}
+isaacgym = {path = "cleanrl/ppo_continuous_action_isaacgym/isaacgym", develop = true}
+
+[tool.poetry.group.dm_control]
+optional = true
+[tool.poetry.group.dm_control.dependencies]
+shimmy = "^0.1.0"
+dm-control = "^1.0.8"
+mujoco = "^2.2"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry.extras]
+atari = ["ale-py", "AutoROM", "opencv-python"]
+pybullet = ["pybullet"]
+procgen = ["procgen"]
+plot = ["pandas", "seaborn"]
+pytest = ["pytest"]
+mujoco = ["mujoco", "imageio"]
+mujoco_py = ["free-mujoco-py"]
+jax = ["jax", "jaxlib", "flax"]
+docs = ["mkdocs-material", "markdown-include"]
+envpool = ["envpool"]
+optuna = ["optuna", "optuna-dashboard", "rich"]
+pettingzoo = ["PettingZoo", "SuperSuit", "multi-agent-ale-py"]
+cloud = ["boto3", "awscli"]
+dm_control = ["shimmy", "dm-control", "mujoco"]
+
+# dependencies for algorithm variant (useful when you want to run a specific algorithm)
+dqn = []
+dqn_atari = ["ale-py", "AutoROM", "opencv-python"]
+dqn_jax = ["jax", "jaxlib", "flax"]
+dqn_atari_jax = [
+    "ale-py", "AutoROM", "opencv-python", # atari
+    "jax", "jaxlib", "flax" # jax
+]
+c51 = []
+c51_atari = ["ale-py", "AutoROM", "opencv-python"]
+c51_jax = ["jax", "jaxlib", "flax"]
+c51_atari_jax = [
+    "ale-py", "AutoROM", "opencv-python", # atari
+    "jax", "jaxlib", "flax" # jax
+]
+ppo_atari_envpool_xla_jax_scan = [
+    "ale-py", "AutoROM", "opencv-python", # atari
+    "jax", "jaxlib", "flax", # jax
+    "envpool", # envpool
+]