pushing model

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
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+ppo_atari_envpool_xla_jax_scan.cleanrl_model filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+tags:
+- Breakout-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: Breakout-v5
+      type: Breakout-v5
+    metrics:
+    - type: mean_reward
+      value: 352.90 +/- 96.78
+      name: mean_reward
+      verified: false
+---
+
+# (CleanRL) **PPO** Agent Playing **Breakout-v5**
+
+This is a trained model of a PPO agent playing Breakout-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_xla_jax_scan.py).
+
+## Get Started
+
+To use this model, please install the `cleanrl` package with the following command:
+
+```
+pip install "cleanrl[ppo_atari_envpool_xla_jax_scan]"
+python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_xla_jax_scan --env-id Breakout-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/Breakout-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/ppo_atari_envpool_xla_jax_scan.py
+curl -OL https://huggingface.co/cleanrl/Breakout-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/pyproject.toml
+curl -OL https://huggingface.co/cleanrl/Breakout-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/poetry.lock
+poetry install --all-extras
+python ppo_atari_envpool_xla_jax_scan.py --track --save-model --upload-model --hf-entity cleanrl --env-id Breakout-v5 --seed 1
+```
+
+# Hyperparameters
+```python
+{'anneal_lr': True,
+ 'batch_size': 1024,
+ 'capture_video': False,
+ 'clip_coef': 0.1,
+ 'cuda': True,
+ 'ent_coef': 0.01,
+ 'env_id': 'Breakout-v5',
+ 'exp_name': 'ppo_atari_envpool_xla_jax_scan',
+ 'gae_lambda': 0.95,
+ 'gamma': 0.99,
+ 'hf_entity': 'cleanrl',
+ 'learning_rate': 0.00025,
+ 'max_grad_norm': 0.5,
+ 'minibatch_size': 256,
+ 'norm_adv': True,
+ 'num_envs': 8,
+ 'num_minibatches': 4,
+ 'num_steps': 128,
+ 'num_updates': 9765,
+ 'save_model': True,
+ 'seed': 1,
+ 'target_kl': None,
+ 'torch_deterministic': True,
+ 'total_timesteps': 10000000,
+ 'track': True,
+ 'update_epochs': 4,
+ 'upload_model': True,
+ 'vf_coef': 0.5,
+ 'wandb_entity': None,
+ 'wandb_project_name': 'cleanRL'}
+```
+    

events.out.tfevents.1672533100.pop-os.989457.0

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ppo_atari_envpool_xla_jax_scan.cleanrl_model

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ppo_atari_envpool_xla_jax_scan.py

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+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_envpool_xla_jaxpy
+import argparse
+import os
+import random
+import time
+from distutils.util import strtobool
+from functools import partial
+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="whether 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="Pong-v5",
+        help="the id of the environment")
+    parser.add_argument("--total-timesteps", type=int, default=10000000,
+        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=8,
+        help="the number of parallel game environments")
+    parser.add_argument("--num-steps", type=int, default=128,
+        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("--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=4,
+        help="the number of mini-batches")
+    parser.add_argument("--update-epochs", type=int, default=4,
+        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):
+    def thunk():
+        envs = envpool.make(
+            env_id,
+            env_type="gym",
+            num_envs=num_envs,
+            episodic_life=True,
+            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 Network(nn.Module):
+    @nn.compact
+    def __call__(self, x):
+        x = jnp.transpose(x, (0, 2, 3, 1))
+        x = x / (255.0)
+        x = nn.Conv(
+            32,
+            kernel_size=(8, 8),
+            strides=(4, 4),
+            padding="VALID",
+            kernel_init=orthogonal(np.sqrt(2)),
+            bias_init=constant(0.0),
+        )(x)
+        x = nn.relu(x)
+        x = nn.Conv(
+            64,
+            kernel_size=(4, 4),
+            strides=(2, 2),
+            padding="VALID",
+            kernel_init=orthogonal(np.sqrt(2)),
+            bias_init=constant(0.0),
+        )(x)
+        x = nn.relu(x)
+        x = nn.Conv(
+            64,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding="VALID",
+            kernel_init=orthogonal(np.sqrt(2)),
+            bias_init=constant(0.0),
+        )(x)
+        x = nn.relu(x)
+        x = x.reshape((x.shape[0], -1))
+        x = nn.Dense(512, 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
+
+
+@flax.struct.dataclass
+class Storage:
+    obs: jnp.array
+    actions: jnp.array
+    logprobs: jnp.array
+    dones: jnp.array
+    values: jnp.array
+    advantages: jnp.array
+    returns: jnp.array
+    rewards: jnp.array
+
+
+@flax.struct.dataclass
+class EpisodeStatistics:
+    episode_returns: jnp.array
+    episode_lengths: jnp.array
+    returned_episode_returns: jnp.array
+    returned_episode_lengths: jnp.array
+
+
+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)()
+    episode_stats = EpisodeStatistics(
+        episode_returns=jnp.zeros(args.num_envs, dtype=jnp.float32),
+        episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32),
+        returned_episode_returns=jnp.zeros(args.num_envs, dtype=jnp.float32),
+        returned_episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32),
+    )
+    handle, recv, send, step_env = envs.xla()
+
+    def step_env_wrappeed(episode_stats, handle, action):
+        handle, (next_obs, reward, next_done, info) = step_env(handle, action)
+        new_episode_return = episode_stats.episode_returns + info["reward"]
+        new_episode_length = episode_stats.episode_lengths + 1
+        episode_stats = episode_stats.replace(
+            episode_returns=(new_episode_return) * (1 - info["terminated"]) * (1 - info["TimeLimit.truncated"]),
+            episode_lengths=(new_episode_length) * (1 - info["terminated"]) * (1 - info["TimeLimit.truncated"]),
+            # only update the `returned_episode_returns` if the episode is done
+            returned_episode_returns=jnp.where(
+                info["terminated"] + info["TimeLimit.truncated"], new_episode_return, episode_stats.returned_episode_returns
+            ),
+            returned_episode_lengths=jnp.where(
+                info["terminated"] + info["TimeLimit.truncated"], new_episode_length, episode_stats.returned_episode_lengths
+            ),
+        )
+        return episode_stats, handle, (next_obs, reward, next_done, info)
+
+    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
+            ),
+        ),
+    )
+    network.apply = jax.jit(network.apply)
+    actor.apply = jax.jit(actor.apply)
+    critic.apply = jax.jit(critic.apply)
+
+    @jax.jit
+    def get_action_and_value(
+        agent_state: TrainState,
+        next_obs: np.ndarray,
+        key: jax.random.PRNGKey,
+    ):
+        """sample action, calculate value, logprob, entropy, and update storage"""
+        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(1), key
+
+    @jax.jit
+    def get_action_and_value2(
+        params: flax.core.FrozenDict,
+        x: np.ndarray,
+        action: np.ndarray,
+    ):
+        """calculate value, logprob of supplied `action`, and entropy"""
+        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]
+        # normalize the logits https://gregorygundersen.com/blog/2020/02/09/log-sum-exp/
+        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, inp, gamma, gae_lambda):
+        advantages = carry
+        nextdone, nextvalues, curvalues, reward = inp
+        nextnonterminal = 1.0 - nextdone
+
+        delta = reward + gamma * nextvalues * nextnonterminal - curvalues
+        advantages = delta + gamma * gae_lambda * nextnonterminal * advantages
+        return advantages, advantages
+
+    compute_gae_once = partial(compute_gae_once, gamma=args.gamma, gae_lambda=args.gae_lambda)
+
+    @jax.jit
+    def compute_gae(
+        agent_state: TrainState,
+        next_obs: np.ndarray,
+        next_done: np.ndarray,
+        storage: Storage,
+    ):
+        next_value = critic.apply(
+            agent_state.params.critic_params, network.apply(agent_state.params.network_params, next_obs)
+        ).squeeze()
+
+        advantages = jnp.zeros((args.num_envs,))
+        dones = jnp.concatenate([storage.dones, next_done[None, :]], axis=0)
+        values = jnp.concatenate([storage.values, next_value[None, :]], axis=0)
+        _, advantages = jax.lax.scan(
+            compute_gae_once, advantages, (dones[1:], values[1:], values[:-1], storage.rewards), reverse=True
+        )
+        storage = storage.replace(
+            advantages=advantages,
+            returns=advantages + storage.values,
+        )
+        return storage
+
+    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,
+        storage: Storage,
+        key: jax.random.PRNGKey,
+    ):
+        def update_epoch(carry, unused_inp):
+            agent_state, key = carry
+            key, subkey = jax.random.split(key)
+
+            def flatten(x):
+                return x.reshape((-1,) + x.shape[2:])
+
+            # 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
+
+            flatten_storage = jax.tree_map(flatten, storage)
+            shuffled_storage = jax.tree_map(convert_data, flatten_storage)
+
+            def update_minibatch(agent_state, minibatch):
+                (loss, (pg_loss, v_loss, entropy_loss, approx_kl)), grads = ppo_loss_grad_fn(
+                    agent_state.params,
+                    minibatch.obs,
+                    minibatch.actions,
+                    minibatch.logprobs,
+                    minibatch.advantages,
+                    minibatch.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, shuffled_storage
+            )
+            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, grads) = jax.lax.scan(
+            update_epoch, (agent_state, key), (), length=args.update_epochs
+        )
+        return agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, key
+
+    # TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time = time.time()
+    next_obs = envs.reset()
+    next_done = jnp.zeros(args.num_envs, dtype=jax.numpy.bool_)
+
+    # based on https://github.dev/google/evojax/blob/0625d875262011d8e1b6aa32566b236f44b4da66/evojax/sim_mgr.py
+    def step_once(carry, step, env_step_fn):
+        agent_state, episode_stats, obs, done, key, handle = carry
+        action, logprob, value, key = get_action_and_value(agent_state, obs, key)
+
+        episode_stats, handle, (next_obs, reward, next_done, _) = env_step_fn(episode_stats, handle, action)
+        storage = Storage(
+            obs=obs,
+            actions=action,
+            logprobs=logprob,
+            dones=done,
+            values=value,
+            rewards=reward,
+            returns=jnp.zeros_like(reward),
+            advantages=jnp.zeros_like(reward),
+        )
+        return ((agent_state, episode_stats, next_obs, next_done, key, handle), storage)
+
+    def rollout(agent_state, episode_stats, next_obs, next_done, key, handle, step_once_fn, max_steps):
+        (agent_state, episode_stats, next_obs, next_done, key, handle), storage = jax.lax.scan(
+            step_once_fn, (agent_state, episode_stats, next_obs, next_done, key, handle), (), max_steps
+        )
+        return agent_state, episode_stats, next_obs, next_done, storage, key, handle
+
+    rollout = partial(rollout, step_once_fn=partial(step_once, env_step_fn=step_env_wrappeed), max_steps=args.num_steps)
+
+    for update in range(1, args.num_updates + 1):
+        update_time_start = time.time()
+        agent_state, episode_stats, next_obs, next_done, storage, key, handle = rollout(
+            agent_state, episode_stats, next_obs, next_done, key, handle
+        )
+        global_step += args.num_steps * args.num_envs
+        storage = compute_gae(agent_state, next_obs, next_done, storage)
+        agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, key = update_ppo(
+            agent_state,
+            storage,
+            key,
+        )
+        avg_episodic_return = np.mean(jax.device_get(episode_stats.returned_episode_returns))
+        print(f"global_step={global_step}, avg_episodic_return={avg_episodic_return}")
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/avg_episodic_return", avg_episodic_return, global_step)
+        writer.add_scalar(
+            "charts/avg_episodic_length", np.mean(jax.device_get(episode_stats.returned_episode_lengths)), global_step
+        )
+        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
+        )
+
+    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 = "1.1.0"
+description = "High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features"
+authors = ["Costa Huang <costa.huang@outlook.com>"]
+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 = "^0.6.4", 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 = "^0.6.4"
+
+[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
+]