652 lines
23 KiB
Python
652 lines
23 KiB
Python
import datetime
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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 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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from flax.training.train_state import TrainState
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from experiment_logger import get_logger
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from brittle_star_project.configs.main_config import BrittleStarConfig
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from brittle_star_project.dataclasses import EpisodeStatistics
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from brittle_star_project.environment.BrittleStarJaxEnvWrapper import BrittleStarJaxEnvWrapper
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from brittle_star_project.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 brittle_star_project.ppo import PPO
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# TODO: move to config
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_ALLOWED_OBS_KEYS = {
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"joint_position",
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"joint_velocity",
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"joint_actuator_force",
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"actuator_force",
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"disk_position",
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"disk_rotation",
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"disk_linear_velocity",
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"disk_angular_velocity",
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"unit_xy_direction_to_target",
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"xy_distance_to_target",
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}
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# TODO: clip scaled reward?
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@jax.jit
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def _get_xy_distance_to_target(obs_dict: dict) -> jnp.ndarray:
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"""Extract xy_distance_to_target for all environments."""
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# obs_dict is a dict of arrays with leading batch dimension (num_envs, ...)
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return obs_dict["xy_distance_to_target"].squeeze(-1) # shape: (num_envs,)
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@jax.jit
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def _clip_action(action: jnp.ndarray, low: jnp.ndarray, high: jnp.ndarray) -> jnp.ndarray:
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return jnp.clip(action, low, high)
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def _compute_explained_variance(values: jnp.ndarray, returns: jnp.ndarray) -> float:
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var_returns = jnp.var(returns)
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explained_var = 1.0 - jnp.var(returns - values) / (var_returns + 1e-8)
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return float(explained_var)
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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 _normalize_obs(obs, mean, var, eps=1e-8):
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return jnp.clip((obs - mean) / jnp.sqrt(var + eps), -10.0, 10.0)
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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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"""Convert the raw observation dict → flat array, filtering unwanted keys."""
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def _filter_and_flatten(o: dict) -> jnp.ndarray:
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values = []
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for key in sorted(o.keys()):
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if key in _ALLOWED_OBS_KEYS: # TODO: NORMALIZATION or .. of observations??
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v = o[key]
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if v.size > 0:
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values.append(jnp.asarray(v).flatten())
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return jnp.concatenate(values)
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return jax.vmap(_filter_and_flatten)(obs_dict)
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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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action_low,
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action_high,
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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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mean, log_std = actor.apply(agent_state.params["actor_params"], hidden)
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log_std = jnp.clip(log_std, -5, 2)
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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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raw_action = mean + noise * std
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clipped_action = _clip_action(raw_action, action_low, action_high)
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logprob = -0.5 * (((raw_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 clipped_action, raw_action, logprob, value.squeeze(-1), mean, std, key
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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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action_low,
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action_high,
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):
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agent_state, episode_stats, obs, done, key, env_state = carry
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clipped_action, raw_action, logprob, value, mean, std, key = _get_action_and_value_noise(
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sensor, feature_extractor, actor, critic, agent_state, obs, key, action_low, action_high
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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, clipped_action
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)
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storage = Storage(
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obs=obs,
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actions=raw_action,
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raw_actions=raw_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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means=mean,
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stds=std,
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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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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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reward = next_env_state.reward
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reward *= 20000
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reward = jnp.clip(reward, -10, 10)
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terminated = next_env_state.terminated
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truncated = next_env_state.truncated
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done = terminated | truncated
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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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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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action_low,
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action_high,
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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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action_low=action_low,
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action_high=action_high,
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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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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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def _compute_gae_jit(
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agent_state,
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storage,
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next_obs,
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next_done,
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gamma,
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gae_lambda,
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num_envs,
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feature_extractor,
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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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feature_extractor.apply(agent_state.params["feature_extractor_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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returns = advantages + storage.values
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advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
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return storage.replace(advantages=advantages, returns=returns)
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@dataclass
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class TrainingMeasurements:
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loss: jnp.ndarray
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pg_loss: jnp.ndarray
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v_loss: jnp.ndarray
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entropy_loss: jnp.ndarray
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approx_kl: jnp.ndarray
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avg_episodic_return: float
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explained_variance: float
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num_terminated: int
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num_truncated: int
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avg_terminated_length: Any
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avg_truncated_length: Any
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class PPOTrainer:
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def __init__(
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self, cfg: BrittleStarConfig, env: BrittleStarJaxEnvWrapper, run_dir: str, run_name: str
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):
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self.cfg = cfg
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self.ppo = cfg.ppo
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self.experiment = cfg.experiment
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self.logging_cfg = cfg.logging
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self.env = env
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self.run_dir = run_dir
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self.run_name = run_name
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self.logger = get_logger()
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# Derived runtime fields
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self.batch_size = self.ppo.num_envs * self.ppo.num_steps
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self.num_iterations = self.ppo.total_timesteps // self.batch_size
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self.key = jax.random.PRNGKey(self.experiment.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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action_low = jnp.asarray(self.env.single_action_space.low, dtype=jnp.float32)
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action_high = jnp.asarray(self.env.single_action_space.high, dtype=jnp.float32)
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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.ppo.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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action_low=action_low,
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action_high=action_high,
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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.ppo.num_envs,
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gamma=self.ppo.gamma,
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gae_lambda=self.ppo.gae_lambda,
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feature_extractor=self.feature_extractor,
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critic=self.critic,
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)
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)
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self._ppo = PPO(self.ppo, 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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self.logger.info(f"[RANDOM]: Setting random seed to {self.experiment.seed}")
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random.seed(self.experiment.seed)
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np.random.seed(self.experiment.seed)
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def _init_agent(self):
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self.logger.info("[AGENT]: Initializing agent...")
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sensor = GenericDenseLayersWithActivation(layer_sizes=[300, 300, 300])
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feature_extractor = GenericDenseLayersWithActivation(layer_sizes=[300, 300, 300])
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actor = Actor(action_dim=self.env.single_action_space.shape[0])
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critic = 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.logger.info("[AGENT STATE]: Initializing agent state...")
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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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dummy_reset = self.env.reset(seed=0)
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sample_obs = _convert_obs_dict_to_array(dummy_reset.observations)[0] # take first env
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self.obs_mean = jnp.zeros((len(sample_obs),))
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self.obs_var = jnp.ones((len(sample_obs),))
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self.obs_count = 1e-4
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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.ppo.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.ppo.num_minibatches,
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update_epochs=self.ppo.update_epochs,
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num_iterations=self.num_iterations,
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learning_rate=self.ppo.learning_rate,
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)
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if self.ppo.anneal_lr
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else self.ppo.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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self.logger.info("[EPISODE STATS]: Initializing episode stats...")
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return EpisodeStatistics(
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episode_returns=jnp.zeros(self.ppo.num_envs, dtype=jnp.float32),
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episode_lengths=jnp.zeros(self.ppo.num_envs, dtype=jnp.int32),
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returned_episode_returns=jnp.zeros(self.ppo.num_envs, jnp.float32),
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returned_episode_lengths=jnp.zeros(self.ppo.num_envs, dtype=jnp.int32),
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)
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def _update_obs_stats(self, obs: jnp.ndarray):
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batch_mean = jnp.mean(obs, axis=0)
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batch_var = jnp.var(obs, axis=0)
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batch_count = obs.shape[0]
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delta = batch_mean - self.obs_mean
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total_count = self.obs_count + batch_count
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new_mean = self.obs_mean + delta * batch_count / total_count
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m_a = self.obs_var * self.obs_count
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m_b = batch_var * batch_count
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M2 = m_a + m_b + delta**2 * self.obs_count * batch_count / total_count
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new_var = M2 / total_count
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self.obs_mean = new_mean
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self.obs_var = new_var
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self.obs_count = total_count
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def _rollout(self, env_state, next_obs, next_done) -> tuple[Any, ...]:
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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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training_measurements,
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storage,
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next_obs,
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xy_distance,
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):
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data = jax.device_get(
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{
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"rewards": storage.rewards[0],
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"values": storage.values[0],
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"returns": storage.returns[0],
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"advantages": storage.advantages[0],
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"actions": storage.actions[0],
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"raw_actions": storage.raw_actions[0],
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"means": storage.means[0],
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"stds": storage.stds[0],
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"logprobs": storage.logprobs[0],
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}
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)
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storage_metrics = {
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"rollout/env0/return_mean": float(np.mean(data["returns"])),
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"rollout/env0/advantage_mean": float(np.mean(data["advantages"])),
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"rollout/env0/value_mean": float(np.mean(data["values"])),
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"rollout/env0/value_vs_return_diff": float(np.mean(data["values"] - data["returns"])),
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"rollout/env0/reward_mean": float(np.mean(data["rewards"])),
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"rollout/env0/mean_mean": float(np.mean(data["means"])),
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"rollout/env0/logprob_mean": float(np.mean(data["logprobs"])),
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"rollout/env0/action_mean": float(np.mean(data["actions"])),
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"rollout/env0/raw_action_mean": float(np.mean(data["raw_actions"])),
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}
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for i in range(len(xy_distance)):
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storage_metrics[f"env_data/env{i}_xy_dist_target"] = float(xy_distance[i])
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metrics = {
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"charts/avg_episodic_return": training_measurements.avg_episodic_return,
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"charts/avg_episodic_length": np.mean(
|
|
jax.device_get(episode_stats.returned_episode_lengths)
|
|
),
|
|
"charts/learning_rate": self.agent_state.opt_state[1]
|
|
.hyperparams["learning_rate"]
|
|
.item(),
|
|
"charts/explained_variance": training_measurements.explained_variance,
|
|
"charts/num_terminated": training_measurements.num_terminated,
|
|
"charts/num_truncated": training_measurements.num_truncated,
|
|
"charts/avg_terminated_ep_length": training_measurements.avg_terminated_length,
|
|
"charts/avg_truncated_ep_length": training_measurements.avg_truncated_length,
|
|
"losses/value_loss": training_measurements.v_loss[-1, -1].item(),
|
|
"losses/policy_loss": training_measurements.pg_loss[-1, -1].item(),
|
|
"losses/entropy": training_measurements.entropy_loss[-1, -1].item(),
|
|
"losses/approx_kl": training_measurements.approx_kl[-1, -1].item(),
|
|
"losses/loss": training_measurements.loss[-1, -1].item(),
|
|
"charts/SPS": int(global_step / (time.time() - start_time)),
|
|
"charts/SPS_update": int(
|
|
self.ppo.num_envs * self.ppo.num_steps / (time.time() - iteration_time_start)
|
|
),
|
|
**storage_metrics,
|
|
}
|
|
self.logger.log(metrics, step=global_step)
|
|
|
|
def _step(self, env_state, next_obs, next_done, iteration: int) -> tuple:
|
|
if iteration == 1:
|
|
self.logger.log_non_interactive(f"Starting first rollout (JIT): {time.ctime()}")
|
|
|
|
(
|
|
self.agent_state,
|
|
self.episode_stats,
|
|
next_obs,
|
|
next_done,
|
|
storage,
|
|
self.key,
|
|
next_env_state,
|
|
) = self._rollout(env_state, next_obs, next_done)
|
|
|
|
if iteration == 1:
|
|
self.logger.log_non_interactive(f"First rollout completed: {time.ctime()}")
|
|
|
|
storage = self._compute_gae(storage, next_obs, next_done)
|
|
|
|
if iteration == 1:
|
|
self.logger.log_non_interactive(f"Starting first PPO update (JIT): {time.ctime()}")
|
|
|
|
self.agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, self.key = (
|
|
self._ppo.update_ppo(self.agent_state, storage, self.key)
|
|
)
|
|
|
|
if iteration == 1:
|
|
self.logger.log_non_interactive(f"First PPO update completed: {time.ctime()}")
|
|
|
|
avg_episodic_return = float(
|
|
jnp.mean(jax.device_get(self.episode_stats.returned_episode_returns)).item()
|
|
)
|
|
|
|
explained_var = _compute_explained_variance(storage.values, storage.returns)
|
|
|
|
terminated = next_env_state.terminated
|
|
truncated = next_env_state.truncated
|
|
episode_lengths = self.episode_stats.returned_episode_lengths
|
|
|
|
num_terminated = int(jnp.sum(terminated).item())
|
|
num_truncated = int(jnp.sum(truncated).item())
|
|
|
|
avg_terminated_length = jnp.sum(episode_lengths * terminated) / jnp.maximum(
|
|
jnp.sum(terminated), 1
|
|
)
|
|
|
|
avg_truncated_length = jnp.sum(episode_lengths * truncated) / jnp.maximum(
|
|
jnp.sum(truncated), 1
|
|
)
|
|
|
|
return (
|
|
next_env_state,
|
|
next_obs,
|
|
next_done,
|
|
TrainingMeasurements(
|
|
loss=loss,
|
|
pg_loss=pg_loss,
|
|
v_loss=v_loss,
|
|
entropy_loss=entropy_loss,
|
|
approx_kl=approx_kl,
|
|
avg_episodic_return=avg_episodic_return,
|
|
explained_variance=explained_var,
|
|
num_terminated=num_terminated,
|
|
num_truncated=num_truncated,
|
|
avg_terminated_length=avg_terminated_length,
|
|
avg_truncated_length=avg_truncated_length,
|
|
),
|
|
storage,
|
|
)
|
|
|
|
def _close(self):
|
|
self.env.close()
|
|
|
|
def _save_model(self, model_path: str):
|
|
self.logger.info("[SAVE]: Saving the final model...")
|
|
self.logger.save_final_model(params=self.agent_state.params, metadata=asdict(self.cfg))
|
|
|
|
def _save_checkpoint(self, iteration: int):
|
|
self.logger.info(f"[SAVE]: Saving checkpoint at iteration {iteration}...")
|
|
self.logger.save_checkpoint(
|
|
params=self.agent_state.params, step=iteration, metadata=asdict(self.cfg)
|
|
)
|
|
|
|
def train(self):
|
|
"""
|
|
Train the PPO agent for a specified number of iterations.
|
|
Closes the environment at the end of training.
|
|
"""
|
|
self.logger.info(f"running name: {self.run_name}")
|
|
|
|
self.logger.info("[TRAIN]: Resetting environment...")
|
|
self.logger.log_non_interactive(f"Initial reset started: {time.ctime()}")
|
|
|
|
env_state = self.env.reset(seed=self.experiment.seed)
|
|
next_obs = _convert_obs_dict_to_array(env_state.observations)
|
|
next_done = jnp.zeros(self.ppo.num_envs, dtype=jnp.bool_)
|
|
|
|
self.logger.log_non_interactive(f"Initial reset completed: {time.ctime()}")
|
|
|
|
global_step = 0
|
|
start_time = time.time()
|
|
|
|
iter_bar = self.logger.progress_bar(range(1, self.num_iterations + 1))
|
|
for iteration in iter_bar:
|
|
iteration_time_start = time.time()
|
|
|
|
env_state, next_obs, next_done, training_measurements, storage = self._step(
|
|
env_state, next_obs, next_done, iteration=iteration
|
|
)
|
|
self._update_obs_stats(next_obs)
|
|
next_obs = _normalize_obs(next_obs, self.obs_mean, self.obs_var)
|
|
|
|
xy_distance = _get_xy_distance_to_target(env_state.observations)
|
|
|
|
global_step += self.ppo.num_steps * self.ppo.num_envs
|
|
self._log(
|
|
global_step,
|
|
self.episode_stats,
|
|
start_time,
|
|
iteration_time_start,
|
|
training_measurements,
|
|
storage,
|
|
next_obs,
|
|
xy_distance,
|
|
)
|
|
|
|
sps = int(global_step / (time.time() - start_time))
|
|
remaining_steps = self.ppo.total_timesteps - global_step
|
|
eta_seconds = int(remaining_steps / sps) if sps > 0 else 0
|
|
eta_str = str(datetime.timedelta(seconds=eta_seconds))
|
|
|
|
self.logger.log_non_interactive(
|
|
f"Iteration {iteration}/{self.num_iterations} | "
|
|
f"Step {global_step}/{self.ppo.total_timesteps} | "
|
|
f"SPS {sps} | "
|
|
f"Return {training_measurements.avg_episodic_return:.4f} | "
|
|
f"ETA {eta_str}"
|
|
)
|
|
|
|
if self.logging_cfg.save_checkpoints and self.logging_cfg.checkpoint_frequency > 0:
|
|
if iteration % self.logging_cfg.checkpoint_frequency == 0:
|
|
self._save_checkpoint(iteration)
|
|
|
|
if getattr(self.cfg.experiment, "debug_sanity", False):
|
|
self.logger.info("\n[SANITY CHECK] Successfully completed 1 epoch")
|
|
break
|
|
|
|
if self.logging_cfg.save_model:
|
|
model_path = f"{self.run_dir}/{self.experiment.exp_name}.cleanrl_model"
|
|
self._save_model(model_path=model_path)
|
|
|
|
self._close()
|