Pitfall-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1
/
ppo_atari_envpool_async_jax_scan_impalanet_machado.py
| # 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 | |
| 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 | |
| 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) | |
| 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): | |
| 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] | |
| def __call__(self, x): | |
| return nn.Dense(self.action_dim, kernel_init=orthogonal(0.01), bias_init=constant(0.0))(x) | |
| 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 | |
| ), | |
| ), | |
| ) | |
| 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 | |
| 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, | |
| ) | |
| 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) | |
| 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() | |