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# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_envpool_xla_jaxpy
import argparse
import os
import random
import time
from distutils.util import strtobool
from functools import partial
from typing import Sequence
os.environ[
"XLA_PYTHON_CLIENT_MEM_FRACTION"
] = "0.7" # see https://github.com/google/jax/discussions/6332#discussioncomment-1279991
import envpool
import flax
import flax.linen as nn
import gym
import jax
import jax.numpy as jnp
import numpy as np
import optax
from flax.linen.initializers import constant, orthogonal
from flax.training.train_state import TrainState
from torch.utils.tensorboard import SummaryWriter
def parse_args():
# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
help="the name of this experiment")
parser.add_argument("--seed", type=int, default=1,
help="seed of the experiment")
parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="if toggled, `torch.backends.cudnn.deterministic=False`")
parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="if toggled, cuda will be enabled by default")
parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="if toggled, this experiment will be tracked with Weights and Biases")
parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
help="the wandb's project name")
parser.add_argument("--wandb-entity", type=str, default=None,
help="the entity (team) of wandb's project")
parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="whether to capture videos of the agent performances (check out `videos` folder)")
parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="whether to save model into the `runs/{run_name}` folder")
parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="whether to upload the saved model to huggingface")
parser.add_argument("--hf-entity", type=str, default="",
help="the user or org name of the model repository from the Hugging Face Hub")
# Algorithm specific arguments
parser.add_argument("--env-id", type=str, default="Pong-v5",
help="the id of the environment")
parser.add_argument("--total-timesteps", type=int, default=10000000,
help="total timesteps of the experiments")
parser.add_argument("--learning-rate", type=float, default=2.5e-4,
help="the learning rate of the optimizer")
parser.add_argument("--num-envs", type=int, default=8,
help="the number of parallel game environments")
parser.add_argument("--num-steps", type=int, default=128,
help="the number of steps to run in each environment per policy rollout")
parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="Toggle learning rate annealing for policy and value networks")
parser.add_argument("--gamma", type=float, default=0.99,
help="the discount factor gamma")
parser.add_argument("--gae-lambda", type=float, default=0.95,
help="the lambda for the general advantage estimation")
parser.add_argument("--num-minibatches", type=int, default=4,
help="the number of mini-batches")
parser.add_argument("--update-epochs", type=int, default=4,
help="the K epochs to update the policy")
parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="Toggles advantages normalization")
parser.add_argument("--clip-coef", type=float, default=0.1,
help="the surrogate clipping coefficient")
parser.add_argument("--ent-coef", type=float, default=0.01,
help="coefficient of the entropy")
parser.add_argument("--vf-coef", type=float, default=0.5,
help="coefficient of the value function")
parser.add_argument("--max-grad-norm", type=float, default=0.5,
help="the maximum norm for the gradient clipping")
parser.add_argument("--target-kl", type=float, default=None,
help="the target KL divergence threshold")
args = parser.parse_args()
args.batch_size = int(args.num_envs * args.num_steps)
args.minibatch_size = int(args.batch_size // args.num_minibatches)
args.num_updates = args.total_timesteps // args.batch_size
# fmt: on
return args
def make_env(env_id, seed, num_envs):
def thunk():
envs = envpool.make(
env_id,
env_type="gym",
num_envs=num_envs,
episodic_life=True,
reward_clip=True,
seed=seed,
)
envs.num_envs = num_envs
envs.single_action_space = envs.action_space
envs.single_observation_space = envs.observation_space
envs.is_vector_env = True
return envs
return thunk
class Network(nn.Module):
@nn.compact
def __call__(self, x):
x = jnp.transpose(x, (0, 2, 3, 1))
x = x / (255.0)
x = nn.Conv(
32,
kernel_size=(8, 8),
strides=(4, 4),
padding="VALID",
kernel_init=orthogonal(np.sqrt(2)),
bias_init=constant(0.0),
)(x)
x = nn.relu(x)
x = nn.Conv(
64,
kernel_size=(4, 4),
strides=(2, 2),
padding="VALID",
kernel_init=orthogonal(np.sqrt(2)),
bias_init=constant(0.0),
)(x)
x = nn.relu(x)
x = nn.Conv(
64,
kernel_size=(3, 3),
strides=(1, 1),
padding="VALID",
kernel_init=orthogonal(np.sqrt(2)),
bias_init=constant(0.0),
)(x)
x = nn.relu(x)
x = x.reshape((x.shape[0], -1))
x = nn.Dense(512, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))(x)
x = nn.relu(x)
return x
class Critic(nn.Module):
@nn.compact
def __call__(self, x):
return nn.Dense(1, kernel_init=orthogonal(1), bias_init=constant(0.0))(x)
class Actor(nn.Module):
action_dim: Sequence[int]
@nn.compact
def __call__(self, x):
return nn.Dense(self.action_dim, kernel_init=orthogonal(0.01), bias_init=constant(0.0))(x)
@flax.struct.dataclass
class AgentParams:
network_params: flax.core.FrozenDict
actor_params: flax.core.FrozenDict
critic_params: flax.core.FrozenDict
@flax.struct.dataclass
class Storage:
obs: jnp.array
actions: jnp.array
logprobs: jnp.array
dones: jnp.array
values: jnp.array
advantages: jnp.array
returns: jnp.array
rewards: jnp.array
@flax.struct.dataclass
class EpisodeStatistics:
episode_returns: jnp.array
episode_lengths: jnp.array
returned_episode_returns: jnp.array
returned_episode_lengths: jnp.array
if __name__ == "__main__":
args = parse_args()
run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
if args.track:
import wandb
wandb.init(
project=args.wandb_project_name,
entity=args.wandb_entity,
sync_tensorboard=True,
config=vars(args),
name=run_name,
monitor_gym=True,
save_code=True,
)
writer = SummaryWriter(f"runs/{run_name}")
writer.add_text(
"hyperparameters",
"|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
)
# TRY NOT TO MODIFY: seeding
random.seed(args.seed)
np.random.seed(args.seed)
key = jax.random.PRNGKey(args.seed)
key, network_key, actor_key, critic_key = jax.random.split(key, 4)
# env setup
envs = make_env(args.env_id, args.seed, args.num_envs)()
episode_stats = EpisodeStatistics(
episode_returns=jnp.zeros(args.num_envs, dtype=jnp.float32),
episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32),
returned_episode_returns=jnp.zeros(args.num_envs, dtype=jnp.float32),
returned_episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32),
)
handle, recv, send, step_env = envs.xla()
def step_env_wrappeed(episode_stats, handle, action):
handle, (next_obs, reward, next_done, info) = step_env(handle, action)
new_episode_return = episode_stats.episode_returns + info["reward"]
new_episode_length = episode_stats.episode_lengths + 1
episode_stats = episode_stats.replace(
episode_returns=(new_episode_return) * (1 - info["terminated"]) * (1 - info["TimeLimit.truncated"]),
episode_lengths=(new_episode_length) * (1 - info["terminated"]) * (1 - info["TimeLimit.truncated"]),
# only update the `returned_episode_returns` if the episode is done
returned_episode_returns=jnp.where(
info["terminated"] + info["TimeLimit.truncated"], new_episode_return, episode_stats.returned_episode_returns
),
returned_episode_lengths=jnp.where(
info["terminated"] + info["TimeLimit.truncated"], new_episode_length, episode_stats.returned_episode_lengths
),
)
return episode_stats, handle, (next_obs, reward, next_done, info)
assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
def linear_schedule(count):
# anneal learning rate linearly after one training iteration which contains
# (args.num_minibatches * args.update_epochs) gradient updates
frac = 1.0 - (count // (args.num_minibatches * args.update_epochs)) / args.num_updates
return args.learning_rate * frac
network = Network()
actor = Actor(action_dim=envs.single_action_space.n)
critic = Critic()
network_params = network.init(network_key, np.array([envs.single_observation_space.sample()]))
agent_state = TrainState.create(
apply_fn=None,
params=AgentParams(
network_params,
actor.init(actor_key, network.apply(network_params, np.array([envs.single_observation_space.sample()]))),
critic.init(critic_key, network.apply(network_params, np.array([envs.single_observation_space.sample()]))),
),
tx=optax.chain(
optax.clip_by_global_norm(args.max_grad_norm),
optax.inject_hyperparams(optax.adam)(
learning_rate=linear_schedule if args.anneal_lr else args.learning_rate, eps=1e-5
),
),
)
network.apply = jax.jit(network.apply)
actor.apply = jax.jit(actor.apply)
critic.apply = jax.jit(critic.apply)
@jax.jit
def get_action_and_value(
agent_state: TrainState,
next_obs: np.ndarray,
key: jax.random.PRNGKey,
):
"""sample action, calculate value, logprob, entropy, and update storage"""
hidden = network.apply(agent_state.params.network_params, next_obs)
logits = actor.apply(agent_state.params.actor_params, hidden)
# sample action: Gumbel-softmax trick
# see https://stats.stackexchange.com/questions/359442/sampling-from-a-categorical-distribution
key, subkey = jax.random.split(key)
u = jax.random.uniform(subkey, shape=logits.shape)
action = jnp.argmax(logits - jnp.log(-jnp.log(u)), axis=1)
logprob = jax.nn.log_softmax(logits)[jnp.arange(action.shape[0]), action]
value = critic.apply(agent_state.params.critic_params, hidden)
return action, logprob, value.squeeze(1), key
@jax.jit
def get_action_and_value2(
params: flax.core.FrozenDict,
x: np.ndarray,
action: np.ndarray,
):
"""calculate value, logprob of supplied `action`, and entropy"""
hidden = network.apply(params.network_params, x)
logits = actor.apply(params.actor_params, hidden)
logprob = jax.nn.log_softmax(logits)[jnp.arange(action.shape[0]), action]
# normalize the logits https://gregorygundersen.com/blog/2020/02/09/log-sum-exp/
logits = logits - jax.scipy.special.logsumexp(logits, axis=-1, keepdims=True)
logits = logits.clip(min=jnp.finfo(logits.dtype).min)
p_log_p = logits * jax.nn.softmax(logits)
entropy = -p_log_p.sum(-1)
value = critic.apply(params.critic_params, hidden).squeeze()
return logprob, entropy, value
def compute_gae_once(carry, inp, gamma, gae_lambda):
advantages = carry
nextdone, nextvalues, curvalues, reward = inp
nextnonterminal = 1.0 - nextdone
delta = reward + gamma * nextvalues * nextnonterminal - curvalues
advantages = delta + gamma * gae_lambda * nextnonterminal * advantages
return advantages, advantages
compute_gae_once = partial(compute_gae_once, gamma=args.gamma, gae_lambda=args.gae_lambda)
@jax.jit
def compute_gae(
agent_state: TrainState,
next_obs: np.ndarray,
next_done: np.ndarray,
storage: Storage,
):
next_value = critic.apply(
agent_state.params.critic_params, network.apply(agent_state.params.network_params, next_obs)
).squeeze()
advantages = jnp.zeros((args.num_envs,))
dones = jnp.concatenate([storage.dones, next_done[None, :]], axis=0)
values = jnp.concatenate([storage.values, next_value[None, :]], axis=0)
_, advantages = jax.lax.scan(
compute_gae_once, advantages, (dones[1:], values[1:], values[:-1], storage.rewards), reverse=True
)
storage = storage.replace(
advantages=advantages,
returns=advantages + storage.values,
)
return storage
def ppo_loss(params, x, a, logp, mb_advantages, mb_returns):
newlogprob, entropy, newvalue = get_action_and_value2(params, x, a)
logratio = newlogprob - logp
ratio = jnp.exp(logratio)
approx_kl = ((ratio - 1) - logratio).mean()
if args.norm_adv:
mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
# Policy loss
pg_loss1 = -mb_advantages * ratio
pg_loss2 = -mb_advantages * jnp.clip(ratio, 1 - args.clip_coef, 1 + args.clip_coef)
pg_loss = jnp.maximum(pg_loss1, pg_loss2).mean()
# Value loss
v_loss = 0.5 * ((newvalue - mb_returns) ** 2).mean()
entropy_loss = entropy.mean()
loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
return loss, (pg_loss, v_loss, entropy_loss, jax.lax.stop_gradient(approx_kl))
ppo_loss_grad_fn = jax.value_and_grad(ppo_loss, has_aux=True)
@jax.jit
def update_ppo(
agent_state: TrainState,
storage: Storage,
key: jax.random.PRNGKey,
):
def update_epoch(carry, unused_inp):
agent_state, key = carry
key, subkey = jax.random.split(key)
def flatten(x):
return x.reshape((-1,) + x.shape[2:])
# taken from: https://github.com/google/brax/blob/main/brax/training/agents/ppo/train.py
def convert_data(x: jnp.ndarray):
x = jax.random.permutation(subkey, x)
x = jnp.reshape(x, (args.num_minibatches, -1) + x.shape[1:])
return x
flatten_storage = jax.tree_map(flatten, storage)
shuffled_storage = jax.tree_map(convert_data, flatten_storage)
def update_minibatch(agent_state, minibatch):
(loss, (pg_loss, v_loss, entropy_loss, approx_kl)), grads = ppo_loss_grad_fn(
agent_state.params,
minibatch.obs,
minibatch.actions,
minibatch.logprobs,
minibatch.advantages,
minibatch.returns,
)
agent_state = agent_state.apply_gradients(grads=grads)
return agent_state, (loss, pg_loss, v_loss, entropy_loss, approx_kl, grads)
agent_state, (loss, pg_loss, v_loss, entropy_loss, approx_kl, grads) = jax.lax.scan(
update_minibatch, agent_state, shuffled_storage
)
return (agent_state, key), (loss, pg_loss, v_loss, entropy_loss, approx_kl, grads)
(agent_state, key), (loss, pg_loss, v_loss, entropy_loss, approx_kl, grads) = jax.lax.scan(
update_epoch, (agent_state, key), (), length=args.update_epochs
)
return agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, key
# TRY NOT TO MODIFY: start the game
global_step = 0
start_time = time.time()
next_obs = envs.reset()
next_done = jnp.zeros(args.num_envs, dtype=jax.numpy.bool_)
# based on https://github.dev/google/evojax/blob/0625d875262011d8e1b6aa32566b236f44b4da66/evojax/sim_mgr.py
def step_once(carry, step, env_step_fn):
agent_state, episode_stats, obs, done, key, handle = carry
action, logprob, value, key = get_action_and_value(agent_state, obs, key)
episode_stats, handle, (next_obs, reward, next_done, _) = env_step_fn(episode_stats, handle, action)
storage = Storage(
obs=obs,
actions=action,
logprobs=logprob,
dones=done,
values=value,
rewards=reward,
returns=jnp.zeros_like(reward),
advantages=jnp.zeros_like(reward),
)
return ((agent_state, episode_stats, next_obs, next_done, key, handle), storage)
def rollout(agent_state, episode_stats, next_obs, next_done, key, handle, step_once_fn, max_steps):
(agent_state, episode_stats, next_obs, next_done, key, handle), storage = jax.lax.scan(
step_once_fn, (agent_state, episode_stats, next_obs, next_done, key, handle), (), max_steps
)
return agent_state, episode_stats, next_obs, next_done, storage, key, handle
rollout = partial(rollout, step_once_fn=partial(step_once, env_step_fn=step_env_wrappeed), max_steps=args.num_steps)
for update in range(1, args.num_updates + 1):
update_time_start = time.time()
agent_state, episode_stats, next_obs, next_done, storage, key, handle = rollout(
agent_state, episode_stats, next_obs, next_done, key, handle
)
global_step += args.num_steps * args.num_envs
storage = compute_gae(agent_state, next_obs, next_done, storage)
agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, key = update_ppo(
agent_state,
storage,
key,
)
avg_episodic_return = np.mean(jax.device_get(episode_stats.returned_episode_returns))
print(f"global_step={global_step}, avg_episodic_return={avg_episodic_return}")
# TRY NOT TO MODIFY: record rewards for plotting purposes
writer.add_scalar("charts/avg_episodic_return", avg_episodic_return, global_step)
writer.add_scalar(
"charts/avg_episodic_length", np.mean(jax.device_get(episode_stats.returned_episode_lengths)), global_step
)
writer.add_scalar("charts/learning_rate", agent_state.opt_state[1].hyperparams["learning_rate"].item(), global_step)
writer.add_scalar("losses/value_loss", v_loss[-1, -1].item(), global_step)
writer.add_scalar("losses/policy_loss", pg_loss[-1, -1].item(), global_step)
writer.add_scalar("losses/entropy", entropy_loss[-1, -1].item(), global_step)
writer.add_scalar("losses/approx_kl", approx_kl[-1, -1].item(), global_step)
writer.add_scalar("losses/loss", loss[-1, -1].item(), global_step)
print("SPS:", int(global_step / (time.time() - start_time)))
writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
writer.add_scalar(
"charts/SPS_update", int(args.num_envs * args.num_steps / (time.time() - update_time_start)), global_step
)
if args.save_model:
model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
with open(model_path, "wb") as f:
f.write(
flax.serialization.to_bytes(
[
vars(args),
[
agent_state.params.network_params,
agent_state.params.actor_params,
agent_state.params.critic_params,
],
]
)
)
print(f"model saved to {model_path}")
from cleanrl_utils.evals.ppo_envpool_jax_eval import evaluate
episodic_returns = evaluate(
model_path,
make_env,
args.env_id,
eval_episodes=10,
run_name=f"{run_name}-eval",
Model=(Network, Actor, Critic),
)
for idx, episodic_return in enumerate(episodic_returns):
writer.add_scalar("eval/episodic_return", episodic_return, idx)
if args.upload_model:
from cleanrl_utils.huggingface import push_to_hub
repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
push_to_hub(args, episodic_returns, repo_id, "PPO", f"runs/{run_name}", f"videos/{run_name}-eval")
envs.close()
writer.close()
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