350 lines
12 KiB
Text
350 lines
12 KiB
Text
import random
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import time
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from dataclasses import asdict
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from functools import partial
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from typing import Callable
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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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import tyro
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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 PPOArgs
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from brittle_star_project.dataclasses.EpisodeStatistics import EpisodeStatistics
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from brittle_star_project.environment.BrittleStarJaxEnvWrapper import BrittleStarJaxEnvWrapper
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from MLPs.mlps import (
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GenericDenseLayersWithActivation,
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Actor,
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OneDenseLayerMLP,
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AgentParams,
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Storage,
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)
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from plots import simple_plot
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from ppo import PPO
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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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def make_env(config_path: str | None, num_envs: int) -> Callable:
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def thunk():
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if config_path is None:
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return BrittleStarJaxEnvWrapper.default(num_envs=num_envs)
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return BrittleStarJaxEnvWrapper.from_config(config_path, num_envs=num_envs)
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return thunk
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def save_model(model_path: str, agent_state: TrainState, args: PPOArgs):
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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(args),
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[
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agent_state.params["network_params"],
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agent_state.params["actor_params"],
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agent_state.params["critic_params"],
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],
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]
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)
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)
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def train(args: PPOArgs):
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args.batch_size = args.num_envs * args.num_steps
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args.minibatch_size = args.batch_size // args.num_minibatches
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# args.num_iterations = args.total_timesteps // args.batch_size
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args.num_iterations = 5
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run_name = f"{args.exp_name}__seed_{args.seed}__{int(time.time())}"
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print(f"running name: {run_name}")
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if args.track:
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import wandb
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wandb.init(
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project=args.wandb_project_name,
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entity=args.wandb_entity,
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sync_tensorboard=True,
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config=vars(args),
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name=run_name,
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save_code=True,
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)
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writer = SummaryWriter(f"runs/{run_name}")
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writer.add_text(
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"hyperparameters",
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"|param|value|\n|---|---|\n" + "\n".join(f"|{k}|{v}|" for k, v in vars(args).items()),
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)
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random.seed(args.seed)
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np.random.seed(args.seed)
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key = jax.random.PRNGKey(args.seed)
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key, sensor_key, actor_key, critic_key, feature_extractor_key = jax.random.split(key, 5)
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torch.backends.cudnn.deterministic = args.torch_deterministic
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device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
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print(f"Running on device: {device}")
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print("Creating the environment...")
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env = make_env(config_path=args.config_path, num_envs=args.num_envs)()
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print(f"Environment: {env}")
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episode_stats = EpisodeStatistics(
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episode_returns=jnp.zeros(args.num_envs, dtype=jnp.float32),
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episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32),
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returned_episode_returns=jnp.zeros(args.num_envs, jnp.float32),
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returned_episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32),
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)
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def step_env_wrapped(episode_stats: EpisodeStatistics, env_state, action):
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next_env_state = env.step(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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def linear_schedule(count):
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frac = 1.0 - (count // (args.num_minibatches * args.update_epochs)) / args.num_iterations
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return args.learning_rate * frac
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print("Initializing the models...")
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sensor = GenericDenseLayersWithActivation()
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feature_extractor = GenericDenseLayersWithActivation()
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actor = Actor(action_dim=env.single_action_space.shape[0]) # continuous actions for MJX
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critic = OneDenseLayerMLP()
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# messager = OneDenseLayerMLP()
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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 env.single_observation_space.sample(rng=jax.random.PRNGKey(0)).values()
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if v.size > 0
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]
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)
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sensor_params = sensor.init(sensor_key, sample_obs)
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feature_extractor_params = feature_extractor.init(feature_extractor_key, sample_obs)
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actor_params = actor.init(actor_key, sensor.apply(sensor_params, sample_obs))
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critic_params = critic.init(
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critic_key, feature_extractor.apply(feature_extractor_params, sample_obs)
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)
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agent_state = 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(args.max_grad_norm),
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optax.inject_hyperparams(optax.adam)(
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learning_rate=linear_schedule if args.anneal_lr else args.learning_rate, eps=1e-5
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),
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),
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)
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sensor.apply = jax.jit(sensor.apply)
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feature_extractor.apply = jax.jit(feature_extractor.apply)
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actor.apply = jax.jit(actor.apply)
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critic.apply = jax.jit(critic.apply)
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ppo_instance = PPO(args, sensor, actor, critic, feature_extractor)
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@jax.jit
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def get_action_and_value_noise(
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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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# GAE
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@jax.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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@jax.jit
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def compute_gae(agent_state, next_obs, next_done, storage):
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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((args.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=args.gamma, gae_lambda=args.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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# END GAE
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# --- Main training loop ---
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global_step = 0
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start_time = time.time()
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# Reset once to get initial state
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print("Resetting the environment...")
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next_env_state = env.reset(seed=args.seed)
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next_obs = convert_obs_dict_to_array(next_env_state.observations)
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next_done = jnp.zeros(args.num_envs, dtype=jnp.bool_)
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def step_once(carry, _, env_step_fn):
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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(agent_state, obs, key)
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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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def rollout(
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agent_state, episode_stats, next_obs, next_done, key, env_state, step_once_fn, max_steps
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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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step_once_fn,
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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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rollout = partial(
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rollout,
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step_once_fn=partial(step_once, env_step_fn=step_env_wrapped),
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max_steps=args.num_steps,
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)
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print("Starting training...")
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iters_bar = tqdm.tqdm(range(1, args.num_iterations + 1))
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returns = []
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for _ in iters_bar:
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iteration_time_start = time.time()
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agent_state, episode_stats, next_obs, next_done, storage, key, next_env_state = rollout(
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agent_state, episode_stats, next_obs, next_done, key, next_env_state
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)
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global_step += args.num_steps * args.num_envs
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storage = compute_gae(agent_state, next_obs, next_done, storage)
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agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, key = ppo_instance.update_ppo(
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agent_state, storage, key
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)
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avg_episodic_return = np.mean(jax.device_get(episode_stats.returned_episode_returns))
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iters_bar.set_postfix_str(
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f"global_step={global_step}, avg_episodic_return={avg_episodic_return}"
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)
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returns.append(avg_episodic_return)
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writer.add_scalar("charts/avg_episodic_return", avg_episodic_return, global_step)
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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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writer.add_scalar(
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"charts/learning_rate",
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agent_state.opt_state[1].hyperparams["learning_rate"].item(),
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global_step,
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)
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writer.add_scalar("losses/value_loss", v_loss[-1, -1].item(), global_step)
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writer.add_scalar("losses/policy_loss", pg_loss[-1, -1].item(), global_step)
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writer.add_scalar("losses/entropy", entropy_loss[-1, -1].item(), global_step)
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writer.add_scalar("losses/approx_kl", approx_kl[-1, -1].item(), global_step)
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writer.add_scalar("losses/loss", loss[-1, -1].item(), global_step)
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# iters_bar.set_postfix_str(f"SPS: {int(global_step / (time.time() - start_time))}")
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writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
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writer.add_scalar(
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"charts/SPS_update",
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int(args.num_envs * args.num_steps / (time.time() - iteration_time_start)),
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global_step,
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)
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if args.save_model:
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model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
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save_model(model_path, agent_state, args)
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print(f"model saved to {model_path}")
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env.close()
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writer.close()
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print("Saving loss plot...")
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simple_plot(
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list(range(len(returns))),
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returns,
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show_window=True,
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filename=f"runs/{run_name}/{args.exp_name}_losses.png",
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)
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def main() -> None:
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args = tyro.cli(PPOArgs)
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train(args)
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if __name__ == "__main__":
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main()
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