# 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()