Quentin Gallouédec

content

a3dd361 2 months ago

No virus

12.2 kB

	# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/dqn/#dqn_ataripy
	import argparse
	import os
	import random
	import time
	from distutils.util import strtobool

	import gymnasium as gym
	import numpy as np
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import torch.optim as optim
	from stable_baselines3.common.atari_wrappers import (
	ClipRewardEnv,
	EpisodicLifeEnv,
	FireResetEnv,
	MaxAndSkipEnv,
	NoopResetEnv,
	)
	from stable_baselines3.common.buffers import ReplayBuffer
	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="BreakoutNoFrameskip-v4",
	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=1e-4,
	help="the learning rate of the optimizer")
	parser.add_argument("--num-envs", type=int, default=1,
	help="the number of parallel game environments")
	parser.add_argument("--buffer-size", type=int, default=1000000,
	help="the replay memory buffer size")
	parser.add_argument("--gamma", type=float, default=0.99,
	help="the discount factor gamma")
	parser.add_argument("--tau", type=float, default=1.,
	help="the target network update rate")
	parser.add_argument("--target-network-frequency", type=int, default=1000,
	help="the timesteps it takes to update the target network")
	parser.add_argument("--batch-size", type=int, default=32,
	help="the batch size of sample from the reply memory")
	parser.add_argument("--start-e", type=float, default=1,
	help="the starting epsilon for exploration")
	parser.add_argument("--end-e", type=float, default=0.01,
	help="the ending epsilon for exploration")
	parser.add_argument("--exploration-fraction", type=float, default=0.10,
	help="the fraction of `total-timesteps` it takes from start-e to go end-e")
	parser.add_argument("--learning-starts", type=int, default=80000,
	help="timestep to start learning")
	parser.add_argument("--train-frequency", type=int, default=4,
	help="the frequency of training")
	args = parser.parse_args()
	# fmt: on
	assert args.num_envs == 1, "vectorized envs are not supported at the moment"

	return args


	def make_env(env_id, seed, 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)

	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)

	env.action_space.seed(seed)
	return env

	return thunk


	# ALGO LOGIC: initialize agent here:
	class QNetwork(nn.Module):
	def __init__(self, env):
	super().__init__()
	self.network = nn.Sequential(
	nn.Conv2d(4, 32, 8, stride=4),
	nn.ReLU(),
	nn.Conv2d(32, 64, 4, stride=2),
	nn.ReLU(),
	nn.Conv2d(64, 64, 3, stride=1),
	nn.ReLU(),
	nn.Flatten(),
	nn.Linear(3136, 512),
	nn.ReLU(),
	nn.Linear(512, env.single_action_space.n),
	)

	def forward(self, x):
	return self.network(x / 255.0)


	def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
	slope = (end_e - start_e) / duration
	return max(slope * t + start_e, end_e)


	if __name__ == "__main__":
	import stable_baselines3 as sb3

	if sb3.__version__ < "2.0":
	raise ValueError(
	"""Ongoing migration: run the following command to install the new dependencies:

	poetry run pip install "stable_baselines3==2.0.0a1" "gymnasium[atari,accept-rom-license]==0.28.1" "ale-py==0.8.1"
	"""
	)
	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)
	torch.manual_seed(args.seed)
	torch.backends.cudnn.deterministic = args.torch_deterministic

	device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")

	# env setup
	envs = gym.vector.SyncVectorEnv(
	[make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
	)
	assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"

	q_network = QNetwork(envs).to(device)
	optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate)
	target_network = QNetwork(envs).to(device)
	target_network.load_state_dict(q_network.state_dict())

	rb = ReplayBuffer(
	args.buffer_size,
	envs.single_observation_space,
	envs.single_action_space,
	device,
	optimize_memory_usage=True,
	handle_timeout_termination=False,
	)
	start_time = time.time()

	# TRY NOT TO MODIFY: start the game
	obs, _ = envs.reset(seed=args.seed)
	for global_step in range(args.total_timesteps):
	# ALGO LOGIC: put action logic here
	epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
	if random.random() < epsilon:
	actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
	else:
	q_values = q_network(torch.Tensor(obs).to(device))
	actions = torch.argmax(q_values, dim=1).cpu().numpy()

	# TRY NOT TO MODIFY: execute the game and log data.
	next_obs, rewards, terminations, truncations, infos = envs.step(actions)

	# TRY NOT TO MODIFY: record rewards for plotting purposes
	if "final_info" in infos:
	for info in infos["final_info"]:
	# Skip the envs that are not done
	if "episode" not in info:
	continue
	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)
	writer.add_scalar("charts/epsilon", epsilon, global_step)
	break

	# TRY NOT TO MODIFY: save data to reply buffer; handle `final_observation`
	real_next_obs = next_obs.copy()
	for idx, trunc in enumerate(truncations):
	if trunc:
	real_next_obs[idx] = infos["final_observation"][idx]
	rb.add(obs, real_next_obs, actions, rewards, terminations, infos)

	# TRY NOT TO MODIFY: CRUCIAL step easy to overlook
	obs = next_obs

	# ALGO LOGIC: training.
	if global_step > args.learning_starts:
	if global_step % args.train_frequency == 0:
	data = rb.sample(args.batch_size)
	with torch.no_grad():
	target_max, _ = target_network(data.next_observations).max(dim=1)
	td_target = data.rewards.flatten() + args.gamma * target_max * (1 - data.dones.flatten())
	old_val = q_network(data.observations).gather(1, data.actions).squeeze()
	loss = F.mse_loss(td_target, old_val)

	if global_step % 100 == 0:
	writer.add_scalar("losses/td_loss", loss, global_step)
	writer.add_scalar("losses/q_values", old_val.mean().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)

	# optimize the model
	optimizer.zero_grad()
	loss.backward()
	optimizer.step()

	# update target network
	if global_step % args.target_network_frequency == 0:
	for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()):
	target_network_param.data.copy_(
	args.tau * q_network_param.data + (1.0 - args.tau) * target_network_param.data
	)

	if args.save_model:
	model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
	torch.save(q_network.state_dict(), model_path)
	print(f"model saved to {model_path}")
	from cleanrl_utils.evals.dqn_eval import evaluate

	episodic_returns = evaluate(
	model_path,
	make_env,
	args.env_id,
	eval_episodes=10,
	run_name=f"{run_name}-eval",
	Model=QNetwork,
	device=device,
	epsilon=0.05,
	)
	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, "DQN", f"runs/{run_name}", f"videos/{run_name}-eval")

	envs.close()
	writer.close()