461 lines
16 KiB
Python
461 lines
16 KiB
Python
import random
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import time
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from dataclasses import asdict, dataclass
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from functools import partial
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from typing import Any
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import flax
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import jax
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import jax.numpy as jnp
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import numpy as np
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import optax
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import torch
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import tqdm
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from flax.training.train_state import TrainState
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from torch.utils.tensorboard import SummaryWriter
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from brittle_star_project.dataclasses import EpisodeStatistics, PPOArgs
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from brittle_star_project.environment.BrittleStarJaxEnvWrapper import BrittleStarJaxEnvWrapper
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from MLPs.mlps import (
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Actor,
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AgentParams,
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GenericDenseLayersWithActivation,
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OneDenseLayerMLP,
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Storage,
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)
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from ppo import PPO
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@jax.jit
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def linear_schedule(count, minibatch_count, update_epochs, num_iterations, learning_rate):
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frac = 1.0 - (count // (minibatch_count * update_epochs)) / num_iterations
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return learning_rate * frac
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@jax.jit
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def convert_obs_dict_to_array(obs_dict: dict) -> jnp.ndarray:
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return jax.vmap(lambda o: jnp.concatenate([v.flatten() for v in o.values() if v.size > 0]))(
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obs_dict
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)
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# removed jit: used in _rollout_jit, so will be compiled with _rollout_jit
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def _get_action_and_value_noise(
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sensor: GenericDenseLayersWithActivation,
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feature_extractor: GenericDenseLayersWithActivation,
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actor: Actor,
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critic: OneDenseLayerMLP,
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agent_state: TrainState,
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next_obs: jnp.ndarray,
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key: jax.random.PRNGKey,
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):
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hidden = sensor.apply(agent_state.params["sensor_params"], next_obs)
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hidden_critic = feature_extractor.apply(
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agent_state.params["feature_extractor_params"], next_obs
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)
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# Continuous actions: sample from a Gaussian parameterized by the actor
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mean, log_std = actor.apply(agent_state.params["actor_params"], hidden)
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key, subkey = jax.random.split(key)
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noise = jax.random.normal(subkey, shape=mean.shape)
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std = jnp.exp(log_std)
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action = mean + noise * std
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logprob = -0.5 * (((action - mean) / std) ** 2 + 2 * log_std + jnp.log(2 * jnp.pi)).sum(-1)
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value = critic.apply(agent_state.params["critic_params"], hidden_critic)
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return action, logprob, value.squeeze(-1), key
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# removed jit: used in _rollout_jit, so will be compiled with _rollout_jit
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def _step_once(
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carry,
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_,
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env_step_fn,
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sensor: GenericDenseLayersWithActivation,
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feature_extractor: GenericDenseLayersWithActivation,
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actor: Actor,
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critic: OneDenseLayerMLP,
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):
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agent_state, episode_stats, obs, done, key, env_state = carry
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action, logprob, value, key = _get_action_and_value_noise(
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sensor, feature_extractor, actor, critic, agent_state, obs, key
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)
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episode_stats, env_state, (next_obs, reward, next_done) = env_step_fn(
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episode_stats, env_state, action
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)
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storage = Storage(
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obs=obs,
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actions=action,
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logprobs=logprob,
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dones=done,
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values=value,
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rewards=reward,
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returns=jnp.zeros_like(reward),
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advantages=jnp.zeros_like(reward),
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)
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return (agent_state, episode_stats, next_obs, next_done, key, env_state), storage
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# removed jit: used in _rollout_jit, so will be compiled with _rollout_jit
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def _step_env_wrapped(episode_stats, env_state, action, env_step_fn):
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next_env_state = env_step_fn(env_state, action)
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# Extract per-environment signals from the state object
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reward = next_env_state.reward # (num_envs,)
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terminated = next_env_state.terminated # (num_envs,)
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truncated = next_env_state.truncated # (num_envs,)
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done = terminated | truncated # (num_envs,)
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new_episode_return = episode_stats.episode_returns + reward
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new_episode_length = episode_stats.episode_lengths + 1
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episode_stats = episode_stats.replace(
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episode_returns=new_episode_return * (1 - done),
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episode_lengths=new_episode_length * (1 - done),
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returned_episode_returns=jnp.where(
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done, new_episode_return, episode_stats.returned_episode_returns
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),
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returned_episode_lengths=jnp.where(
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done, new_episode_length, episode_stats.returned_episode_lengths
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),
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)
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return (
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episode_stats,
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next_env_state,
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(convert_obs_dict_to_array(next_env_state.observations), reward, done),
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)
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# jit applied in wrapper method self._rollout_jit using partial
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def _rollout_jit(
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agent_state,
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episode_stats,
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env_state,
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next_obs,
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next_done,
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key,
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max_steps,
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step_env_fn,
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sensor: GenericDenseLayersWithActivation,
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feature_extractor: GenericDenseLayersWithActivation,
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actor: Actor,
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critic: OneDenseLayerMLP,
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):
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(agent_state, episode_stats, next_obs, next_done, key, env_state), storage = jax.lax.scan(
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partial(
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_step_once,
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sensor=sensor,
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feature_extractor=feature_extractor,
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actor=actor,
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critic=critic,
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env_step_fn=step_env_fn,
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),
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(agent_state, episode_stats, next_obs, next_done, key, env_state),
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(),
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max_steps,
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)
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return agent_state, episode_stats, next_obs, next_done, storage, key, env_state
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# removed jit: used in _compute_gae_jit, so will be compiled with _compute_gae_jit
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def _compute_gae_once(carry, inp, gamma, gae_lambda):
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advantages = carry
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nextdone, nextvalues, curvalues, reward = inp
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nextnonterminal = 1.0 - nextdone
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delta = reward + gamma * nextvalues * nextnonterminal - curvalues
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advantages = delta + gamma * gae_lambda * nextnonterminal * advantages
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return advantages, advantages
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# jit applied on partial-wrapped wrapper method self._compute_gae_jit
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def _compute_gae_jit(
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agent_state, storage, next_obs, next_done, gamma, gae_lambda, num_envs, sensor, critic
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):
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next_value = critic.apply(
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agent_state.params["critic_params"],
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sensor.apply(agent_state.params["sensor_params"], next_obs),
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).squeeze(-1)
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advantages = jnp.zeros((num_envs,))
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dones = jnp.concatenate([storage.dones, next_done[None, :]], axis=0)
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values = jnp.concatenate([storage.values, next_value[None, :]], axis=0)
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_, advantages = jax.lax.scan(
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partial(_compute_gae_once, gamma=gamma, gae_lambda=gae_lambda),
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advantages,
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(dones[1:], values[1:], values[:-1], storage.rewards),
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reverse=True,
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)
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return storage.replace(advantages=advantages, returns=advantages + storage.values)
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@dataclass
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class LossInfo:
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# todo: better typing
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loss: Any
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pg_loss: Any
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v_loss: Any
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entropy_loss: Any
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approx_kl: Any
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avg_episodic_return: Any
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class PPOTrainer:
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def __init__(self, args: PPOArgs, env: BrittleStarJaxEnvWrapper, run_name: str):
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self.args = args
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self.env = env
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self.run_name = run_name
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self.writer = SummaryWriter(f"runs/{self.run_name}")
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self.key = jax.random.PRNGKey(args.seed)
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self.sensor, self.feature_extractor, self.actor, self.critic = self._init_agent()
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self.sensor.apply = jax.jit(self.sensor.apply)
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self.feature_extractor.apply = jax.jit(self.feature_extractor.apply)
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self.actor.apply = jax.jit(self.actor.apply)
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self.critic.apply = jax.jit(self.critic.apply)
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self._rollout_jit = jax.jit(
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partial(
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_rollout_jit,
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max_steps=self.args.num_steps,
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step_env_fn=partial(_step_env_wrapped, env_step_fn=self.env.step),
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sensor=self.sensor,
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feature_extractor=self.feature_extractor,
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actor=self.actor,
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critic=self.critic,
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)
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)
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self._compute_gae_jit = jax.jit(
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partial(
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_compute_gae_jit,
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num_envs=self.args.num_envs,
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gamma=self.args.gamma,
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gae_lambda=self.args.gae_lambda,
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sensor=self.sensor,
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critic=self.critic,
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)
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)
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self._ppo = PPO(self.args, self.sensor, self.actor, self.critic, self.feature_extractor)
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self.agent_state = self._init_agent_state()
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self.episode_stats = self._init_episode_stats()
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self._init_random()
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def _init_random(self):
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random.seed(self.args.seed)
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np.random.seed(self.args.seed)
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def _init_agent(self):
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sensor = GenericDenseLayersWithActivation()
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feature_extractor = GenericDenseLayersWithActivation()
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actor = Actor(
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action_dim=self.env.single_action_space.shape[0]
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) # continuous actions for MJX
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critic = OneDenseLayerMLP()
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# messenger = OneDenseLayerMLP()
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return sensor, feature_extractor, actor, critic
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def _init_agent_state(self) -> TrainState:
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self.key, sensor_key, actor_key, critic_key, feature_extractor_key = jax.random.split(
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self.key, 5
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)
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sample_obs = jnp.concatenate(
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[
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v.flatten()
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for v in self.env.single_observation_space.sample(
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rng=jax.random.PRNGKey(0)
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).values()
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if v.size > 0
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]
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)
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sensor_params = self.sensor.init(sensor_key, sample_obs)
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feature_extractor_params = self.feature_extractor.init(feature_extractor_key, sample_obs)
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actor_params = self.actor.init(actor_key, self.sensor.apply(sensor_params, sample_obs))
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critic_params = self.critic.init(
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critic_key, self.feature_extractor.apply(feature_extractor_params, sample_obs)
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)
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return TrainState.create(
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apply_fn=None,
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params=asdict(
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AgentParams(sensor_params, actor_params, critic_params, feature_extractor_params)
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),
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tx=optax.chain(
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optax.clip_by_global_norm(self.args.max_grad_norm),
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optax.inject_hyperparams(optax.adam)(
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learning_rate=partial(
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linear_schedule,
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minibatch_count=self.args.num_minibatches,
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update_epochs=self.args.update_epochs,
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num_iterations=self.args.num_iterations,
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learning_rate=self.args.learning_rate,
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)
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if self.args.anneal_lr
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else self.args.learning_rate,
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eps=1e-5,
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),
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),
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)
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def _init_episode_stats(self) -> EpisodeStatistics:
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return EpisodeStatistics(
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episode_returns=jnp.zeros(self.args.num_envs, dtype=jnp.float32),
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episode_lengths=jnp.zeros(self.args.num_envs, dtype=jnp.int32),
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returned_episode_returns=jnp.zeros(self.args.num_envs, jnp.float32),
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returned_episode_lengths=jnp.zeros(self.args.num_envs, dtype=jnp.int32),
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)
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def _rollout(self, env_state, next_obs, next_done) -> tuple[Storage, ...]:
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return self._rollout_jit(
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self.agent_state,
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self.episode_stats,
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env_state,
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next_obs,
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next_done,
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self.key,
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)
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def _compute_gae(self, storage, next_obs, next_done) -> Storage:
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return self._compute_gae_jit(
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self.agent_state,
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storage,
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next_obs,
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next_done,
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)
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def _log(
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self,
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global_step,
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episode_stats,
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start_time,
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iteration_time_start,
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loss_info,
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):
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self.writer.add_scalar(
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"charts/avg_episodic_return", loss_info.avg_episodic_return, global_step
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)
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self.writer.add_scalar(
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"charts/avg_episodic_length",
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np.mean(jax.device_get(episode_stats.returned_episode_lengths)),
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global_step,
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)
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self.writer.add_scalar(
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"charts/learning_rate",
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self.agent_state.opt_state[1].hyperparams["learning_rate"].item(),
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global_step,
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)
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self.writer.add_scalar("losses/value_loss", loss_info.v_loss[-1, -1].item(), global_step)
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self.writer.add_scalar("losses/policy_loss", loss_info.pg_loss[-1, -1].item(), global_step)
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self.writer.add_scalar("losses/entropy", loss_info.entropy_loss[-1, -1].item(), global_step)
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self.writer.add_scalar("losses/approx_kl", loss_info.approx_kl[-1, -1].item(), global_step)
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self.writer.add_scalar("losses/loss", loss_info.loss[-1, -1].item(), global_step)
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self.writer.add_scalar(
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"charts/SPS", int(global_step / (time.time() - start_time)), global_step
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)
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self.writer.add_scalar(
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"charts/SPS_update",
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int(self.args.num_envs * self.args.num_steps / (time.time() - iteration_time_start)),
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global_step,
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)
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def _step(self, env_state, next_obs, next_done) -> tuple:
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(
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self.agent_state,
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self.episode_stats,
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next_obs,
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next_done,
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storage,
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self.key,
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next_env_state,
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) = self._rollout(env_state, next_obs, next_done)
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storage = self._compute_gae(storage, next_obs, next_done)
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self.agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, self.key = (
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self._ppo.update_ppo(self.agent_state, storage, self.key)
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)
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avg_episodic_return = float(jnp.mean(jax.device_get(self.episode_stats.returned_episode_returns)))
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return (
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next_env_state,
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next_obs,
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next_done,
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LossInfo(
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loss=loss,
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pg_loss=pg_loss,
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v_loss=v_loss,
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entropy_loss=entropy_loss,
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approx_kl=approx_kl,
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avg_episodic_return=avg_episodic_return,
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),
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)
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def _close(self):
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self.env.close()
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self.writer.close()
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def _save_model(self, model_path: str):
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with open(model_path, "wb") as f:
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f.write(
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flax.serialization.to_bytes(
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[
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vars(self.args),
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[
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self.agent_state.params["sensor_params"],
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self.agent_state.params["actor_params"],
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self.agent_state.params["critic_params"],
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self.agent_state.params["feature_extractor_params"],
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],
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]
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)
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)
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def train(self):
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"""
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Train the PPO agent for a specified number of iterations
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(passed through PPOArgs in constructor).
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Closes the environment at the end of training.
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"""
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env_state = self.env.reset(seed=self.args.seed)
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next_obs = convert_obs_dict_to_array(env_state.observations)
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next_done = jnp.zeros(self.args.num_envs, dtype=jnp.bool_)
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global_step = 0
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start_time = time.time()
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if self.args.track:
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import wandb
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wandb.init(
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project=self.args.wandb_project_name,
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entity=self.args.wandb_entity,
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sync_tensorboard=True,
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config=vars(self.args),
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name=self.run_name,
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save_code=True,
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)
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self.writer.add_text(
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"hyperparameters",
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"|param|value|\n|---|---|\n"
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+ "\n".join(f"|{k}|{v}|" for k, v in vars(self.args).items()),
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)
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for _ in tqdm.tqdm(range(self.args.num_iterations)):
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iteration_time_start = time.time()
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env_state, next_obs, next_done, loss_info = self._step(env_state, next_obs, next_done)
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global_step += self.args.num_steps * self.args.num_envs
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self._log(global_step, self.episode_stats, start_time, iteration_time_start, loss_info)
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if self.args.save_model:
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model_path = f"runs/{self.run_name}/{self.args.exp_name}.cleanrl_model"
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self._save_model(model_path=model_path)
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self._close()
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