pushing model

b9634be about 1 year ago

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	# 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 sys
	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
	sys.path.append('c:\\Users\\Retsal\\Documents\\ARF\\cleanrl')
	from cleanrl_utils.evals.dqn_eval import evaluate

	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, terminated, truncated, 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)

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