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February 10, 2024 23:04
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# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_multigpupy | |
import os | |
import random | |
import time | |
import warnings | |
from dataclasses import dataclass, field | |
from typing import List, Literal | |
import gymnasium as gym | |
import numpy as np | |
import torch | |
from accelerate import Accelerator | |
import torch.nn as nn | |
import torch.optim as optim | |
import tyro | |
from rich.pretty import pprint | |
from torch.distributions.categorical import Categorical | |
from torch.utils.tensorboard import SummaryWriter | |
from stable_baselines3.common.atari_wrappers import ( # isort:skip | |
ClipRewardEnv, | |
EpisodicLifeEnv, | |
FireResetEnv, | |
MaxAndSkipEnv, | |
NoopResetEnv, | |
) | |
@dataclass | |
class Args: | |
exp_name: str = os.path.basename(__file__)[: -len(".py")] | |
"""the name of this experiment""" | |
seed: int = 1 | |
"""seed of the experiment""" | |
torch_deterministic: bool = True | |
"""if toggled, `torch.backends.cudnn.deterministic=False`""" | |
cuda: bool = True | |
"""if toggled, cuda will be enabled by default""" | |
track: bool = False | |
"""if toggled, this experiment will be tracked with Weights and Biases""" | |
wandb_project_name: str = "cleanRL" | |
"""the wandb's project name""" | |
wandb_entity: str = None | |
"""the entity (team) of wandb's project""" | |
capture_video: bool = False | |
"""whether to capture videos of the agent performances (check out `videos` folder)""" | |
# Algorithm specific arguments | |
env_id: str = "BreakoutNoFrameskip-v4" | |
"""the id of the environment""" | |
total_timesteps: int = 10000000 | |
"""total timesteps of the experiments""" | |
learning_rate: float = 2.5e-4 | |
"""the learning rate of the optimizer""" | |
local_num_envs: int = 8 | |
"""the number of parallel game environments (in the local rank)""" | |
num_steps: int = 128 | |
"""the number of steps to run in each environment per policy rollout""" | |
anneal_lr: bool = True | |
"""Toggle learning rate annealing for policy and value networks""" | |
gamma: float = 0.99 | |
"""the discount factor gamma""" | |
gae_lambda: float = 0.95 | |
"""the lambda for the general advantage estimation""" | |
num_minibatches: int = 4 | |
"""the number of mini-batches""" | |
update_epochs: int = 4 | |
"""the K epochs to update the policy""" | |
norm_adv: bool = True | |
"""Toggles advantages normalization""" | |
clip_coef: float = 0.1 | |
"""the surrogate clipping coefficient""" | |
clip_vloss: bool = True | |
"""Toggles whether or not to use a clipped loss for the value function, as per the paper.""" | |
ent_coef: float = 0.01 | |
"""coefficient of the entropy""" | |
vf_coef: float = 0.5 | |
"""coefficient of the value function""" | |
max_grad_norm: float = 0.5 | |
"""the maximum norm for the gradient clipping""" | |
target_kl: float = None | |
"""the target KL divergence threshold""" | |
# to be filled in runtime | |
local_batch_size: int = 0 | |
"""the local batch size in the local rank (computed in runtime)""" | |
local_minibatch_size: int = 0 | |
"""the local mini-batch size in the local rank (computed in runtime)""" | |
num_envs: int = 0 | |
"""the number of parallel game environments (computed in runtime)""" | |
batch_size: int = 0 | |
"""the batch size (computed in runtime)""" | |
minibatch_size: int = 0 | |
"""the mini-batch size (computed in runtime)""" | |
num_iterations: int = 0 | |
"""the number of iterations (computed in runtime)""" | |
world_size: int = 0 | |
"""the number of processes (computed in runtime)""" | |
def make_env(env_id, idx, capture_video, run_name): | |
def thunk(): | |
if capture_video and idx == 0: | |
env = gym.make(env_id, render_mode="rgb_array") | |
env = gym.wrappers.RecordVideo(env, f"videos/{run_name}") | |
else: | |
env = gym.make(env_id) | |
env = gym.wrappers.RecordEpisodeStatistics(env) | |
if capture_video: | |
if idx == 0: | |
env = gym.wrappers.RecordVideo(env, f"videos/{run_name}") | |
env = NoopResetEnv(env, noop_max=30) | |
env = MaxAndSkipEnv(env, skip=4) | |
env = EpisodicLifeEnv(env) | |
if "FIRE" in env.unwrapped.get_action_meanings(): | |
env = FireResetEnv(env) | |
env = ClipRewardEnv(env) | |
env = gym.wrappers.ResizeObservation(env, (84, 84)) | |
env = gym.wrappers.GrayScaleObservation(env) | |
env = gym.wrappers.FrameStack(env, 4) | |
return env | |
return thunk | |
def layer_init(layer, std=np.sqrt(2), bias_const=0.0): | |
torch.nn.init.orthogonal_(layer.weight, std) | |
torch.nn.init.constant_(layer.bias, bias_const) | |
return layer | |
class Agent(nn.Module): | |
def __init__(self, envs): | |
super().__init__() | |
self.network = nn.Sequential( | |
layer_init(nn.Conv2d(4, 32, 8, stride=4)), | |
nn.ReLU(), | |
layer_init(nn.Conv2d(32, 64, 4, stride=2)), | |
nn.ReLU(), | |
layer_init(nn.Conv2d(64, 64, 3, stride=1)), | |
nn.ReLU(), | |
nn.Flatten(), | |
layer_init(nn.Linear(64 * 7 * 7, 512)), | |
nn.ReLU(), | |
) | |
self.actor = layer_init(nn.Linear(512, envs.single_action_space.n), std=0.01) | |
self.critic = layer_init(nn.Linear(512, 1), std=1) | |
def get_value(self, x): | |
return self.critic(self.network(x / 255.0)) | |
def get_action_and_value(self, x, action=None): | |
hidden = self.network(x / 255.0) | |
logits = self.actor(hidden) | |
probs = Categorical(logits=logits) | |
if action is None: | |
action = probs.sample() | |
return action, probs.log_prob(action), probs.entropy(), self.critic(hidden) | |
if __name__ == "__main__": | |
# torchrun --standalone --nnodes=1 --nproc_per_node=2 ppo_atari_multigpu.py | |
# taken from https://pytorch.org/docs/stable/elastic/run.html | |
args = tyro.cli(Args) | |
accelerator = Accelerator() | |
local_rank = accelerator.process_index | |
args.world_size = accelerator.num_processes | |
args.local_batch_size = int(args.local_num_envs * args.num_steps) | |
args.local_minibatch_size = int(args.local_batch_size // args.num_minibatches) | |
args.num_envs = args.local_num_envs * args.world_size | |
args.batch_size = int(args.num_envs * args.num_steps) | |
args.minibatch_size = int(args.batch_size // args.num_minibatches) | |
args.num_iterations = args.total_timesteps // args.batch_size | |
run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}" | |
writer = None | |
if local_rank == 0: | |
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()])), | |
) | |
pprint(args) | |
# TRY NOT TO MODIFY: seeding | |
# CRUCIAL: note that we needed to pass a different seed for each data parallelism worker | |
args.seed += accelerator.process_index * 100003 # Prime | |
random.seed(args.seed) | |
np.random.seed(args.seed) | |
torch.manual_seed(args.seed - local_rank) | |
torch.backends.cudnn.deterministic = args.torch_deterministic | |
if len(args.device_ids) > 0: | |
assert len(args.device_ids) == args.world_size, "you must specify the same number of device ids as `--nproc_per_node`" | |
device = torch.device(f"cuda:{args.device_ids[local_rank]}" if torch.cuda.is_available() and args.cuda else "cpu") | |
else: | |
device_count = torch.cuda.device_count() | |
if device_count < args.world_size: | |
device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") | |
else: | |
device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() and args.cuda else "cpu") | |
# env setup | |
envs = gym.vector.SyncVectorEnv( | |
[make_env(args.env_id, i, args.capture_video, run_name) for i in range(args.local_num_envs)], | |
) | |
assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported" | |
agent = Agent(envs).to(device) | |
torch.manual_seed(args.seed) | |
optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5) | |
agent, optimizer = accelerator.prepare(agent, optimizer) | |
# ALGO Logic: Storage setup | |
obs = torch.zeros((args.num_steps, args.local_num_envs) + envs.single_observation_space.shape).to(device) | |
actions = torch.zeros((args.num_steps, args.local_num_envs) + envs.single_action_space.shape).to(device) | |
logprobs = torch.zeros((args.num_steps, args.local_num_envs)).to(device) | |
rewards = torch.zeros((args.num_steps, args.local_num_envs)).to(device) | |
dones = torch.zeros((args.num_steps, args.local_num_envs)).to(device) | |
values = torch.zeros((args.num_steps, args.local_num_envs)).to(device) | |
# TRY NOT TO MODIFY: start the game | |
global_step = 0 | |
start_time = time.time() | |
next_obs, _ = envs.reset(seed=args.seed) | |
next_obs = torch.Tensor(next_obs).to(device) | |
next_done = torch.zeros(args.local_num_envs).to(device) | |
for iteration in range(1, args.num_iterations + 1): | |
# Annealing the rate if instructed to do so. | |
if args.anneal_lr: | |
frac = 1.0 - (iteration - 1.0) / args.num_iterations | |
lrnow = frac * args.learning_rate | |
optimizer.param_groups[0]["lr"] = lrnow | |
for step in range(0, args.num_steps): | |
global_step += args.num_envs | |
obs[step] = next_obs | |
dones[step] = next_done | |
# ALGO LOGIC: action logic | |
with torch.no_grad(): | |
action, logprob, _, value = agent.get_action_and_value(next_obs) | |
values[step] = value.flatten() | |
actions[step] = action | |
logprobs[step] = logprob | |
# TRY NOT TO MODIFY: execute the game and log data. | |
next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy()) | |
next_done = np.logical_or(terminations, truncations) | |
rewards[step] = torch.tensor(reward).to(device).view(-1) | |
next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device) | |
if not writer: | |
continue | |
if "final_info" in infos: | |
for info in infos["final_info"]: | |
if info and "episode" in info: | |
print(f"global_step={global_step}, episodic_return={info['episode']['r']}") | |
writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step) | |
writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step) | |
print( | |
f"local_rank: {local_rank}, action.sum(): {action.sum()}, iteration: {iteration}, agent.actor.weight.sum(): {agent.actor.weight.sum()}" | |
) | |
# bootstrap value if not done | |
with torch.no_grad(): | |
next_value = agent.get_value(next_obs).reshape(1, -1) | |
advantages = torch.zeros_like(rewards).to(device) | |
lastgaelam = 0 | |
for t in reversed(range(args.num_steps)): | |
if t == args.num_steps - 1: | |
nextnonterminal = 1.0 - next_done | |
nextvalues = next_value | |
else: | |
nextnonterminal = 1.0 - dones[t + 1] | |
nextvalues = values[t + 1] | |
delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t] | |
advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam | |
returns = advantages + values | |
# flatten the batch | |
b_obs = obs.reshape((-1,) + envs.single_observation_space.shape) | |
b_logprobs = logprobs.reshape(-1) | |
b_actions = actions.reshape((-1,) + envs.single_action_space.shape) | |
b_advantages = advantages.reshape(-1) | |
b_returns = returns.reshape(-1) | |
b_values = values.reshape(-1) | |
# Optimizing the policy and value network | |
b_inds = np.arange(args.local_batch_size) | |
clipfracs = [] | |
for epoch in range(args.update_epochs): | |
np.random.shuffle(b_inds) | |
for start in range(0, args.local_batch_size, args.local_minibatch_size): | |
end = start + args.local_minibatch_size | |
mb_inds = b_inds[start:end] | |
_, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions.long()[mb_inds]) | |
logratio = newlogprob - b_logprobs[mb_inds] | |
ratio = logratio.exp() | |
with torch.no_grad(): | |
# calculate approx_kl http://joschu.net/blog/kl-approx.html | |
old_approx_kl = (-logratio).mean() | |
approx_kl = ((ratio - 1) - logratio).mean() | |
clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()] | |
mb_advantages = b_advantages[mb_inds] | |
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 * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef) | |
pg_loss = torch.max(pg_loss1, pg_loss2).mean() | |
# Value loss | |
newvalue = newvalue.view(-1) | |
if args.clip_vloss: | |
v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2 | |
v_clipped = b_values[mb_inds] + torch.clamp( | |
newvalue - b_values[mb_inds], | |
-args.clip_coef, | |
args.clip_coef, | |
) | |
v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2 | |
v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped) | |
v_loss = 0.5 * v_loss_max.mean() | |
else: | |
v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean() | |
entropy_loss = entropy.mean() | |
loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef | |
optimizer.zero_grad() | |
accelerator.backward(loss) | |
accelerator.clip_grad_norm_(agent.parameters(), args.max_grad_norm) | |
optimizer.step() | |
if args.target_kl is not None and approx_kl > args.target_kl: | |
break | |
y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy() | |
var_y = np.var(y_true) | |
explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y | |
# TRY NOT TO MODIFY: record rewards for plotting purposes | |
if local_rank == 0: | |
writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step) | |
writer.add_scalar("losses/value_loss", v_loss.item(), global_step) | |
writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step) | |
writer.add_scalar("losses/entropy", entropy_loss.item(), global_step) | |
writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step) | |
writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step) | |
writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step) | |
writer.add_scalar("losses/explained_variance", explained_var, 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) | |
envs.close() | |
if local_rank == 0: | |
writer.close() | |
if args.track: | |
wandb.finish() |
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