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

	import 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.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="CartPole-v1",
	help="the id of the environment")
	parser.add_argument("--total-timesteps", type=int, default=500000,
	help="total timesteps of the experiments")
	parser.add_argument("--learning-rate", type=float, default=0.0001,
	help="the learning rate of the optimizer")
	parser.add_argument("--max-gradient-norm", type=float, default=float('inf'),
	help="gradient clipping value")
	parser.add_argument("--double-learning", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
	help="enable double learning DDQN")
	parser.add_argument("--buffer-size", type=int, default=300000,
	help="the replay memory buffer size")
	parser.add_argument("--gamma", type=float, default=1.0,
	help="the discount factor gamma")
	parser.add_argument("--target-tau", type=float, default=1.0,
	help="the target network update rate")
	parser.add_argument("--target-network-frequency", type=int, default=100,
	help="the timesteps it takes to update the target network")
	parser.add_argument("--batch-size", type=int, default=256,
	help="the batch size of sample from the reply memory")
	parser.add_argument("--start-e", type=float, default=1.0,
	help="the starting epsilon for exploration")
	parser.add_argument("--end-e", type=float, default=0.1,
	help="the ending epsilon for exploration")
	parser.add_argument("--exploration-fraction", type=float, default=0.2,
	help="the fraction of `total-timesteps` it takes from start-e to go end-e")
	parser.add_argument("--learning-starts", type=int, default=1000,
	help="timestep to start learning")
	parser.add_argument("--train-frequency", type=int, default=1,
	help="the frequency of training")
	args = parser.parse_args()
	# fmt: on
	return args


	def make_env(env_id, seed, idx, capture_video, run_name):
	def thunk():
	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.seed(seed)
	env.action_space.seed(seed)
	env.observation_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.Linear(np.array(env.single_observation_space.shape).prod(), 512),
	nn.ReLU(),
	nn.Linear(512, 128),
	nn.ReLU(),
	nn.Linear(128, env.single_action_space.n),
	)

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


	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__":
	args = parse_args()
	run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
	if args.track:
	import wandb

	args.alg_type = os.path.basename(__file__)
	wandb_sess = wandb.init(
	project=args.wandb_project_name,
	entity=args.wandb_entity,
	config=vars(args),
	save_code=True,
	# group='string',
	name=run_name,
	sync_tensorboard=False,
	monitor_gym=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()])),
	)

	def log_value(name: str, x: float, y: int):
	# writer.add_scalar(name, x, y)
	wandb.log({name: x, "global_step": y})

	# TRY NOT TO MODIFY: seeding
	torch.manual_seed(args.seed)
	# torch.backends.cudnn.deterministic = args.torch_deterministic
	np.random.seed(args.seed)
	random.seed(args.seed)

	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, 0, args.capture_video, run_name)])
	assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"

	q_network = QNetwork(envs).to(device)
	optimizer = optim.RMSprop(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=True,
	)
	start_time = time.time()
	target_update_counter = 0
	policy_update_counter = 0
	episode_returns = []

	# TRY NOT TO MODIFY: start the game
	obs = envs.reset()
	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, dones, infos = envs.step(actions)

	# TRY NOT TO MODIFY: record rewards for plotting purposes
	for info in infos:
	if "episode" in info.keys():
	episode_returns.append(info['episode']['r'])
	episode_returns = episode_returns[-100:]
	print(f"step={global_step}, return={info['episode']['r']}, sps={int(global_step / (time.time() - start_time))}")
	log_value("perf/episodic_return", info["episode"]["r"], global_step)
	log_value("perf/episodic_return_mean_100", np.mean(episode_returns), global_step)
	log_value("perf/episodic_return_std_100", np.std(episode_returns), global_step)
	log_value("debug/episodic_length", info["episode"]["l"], global_step)
	log_value("ex2/epsilon", epsilon, global_step)
	break

	# TRY NOT TO MODIFY: save data to reply buffer; handle `terminal_observation`
	real_next_obs = next_obs.copy()
	for idx, d in enumerate(dones):
	if d:
	real_next_obs[idx] = infos[idx]["terminal_observation"]
	rb.add(obs, real_next_obs, actions, rewards, dones, 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():
	if args.double_learning:
	argmax_a = q_network(data.observatons).max(1)[1]
	else:
	argmax_a = target_network(data.observatons).max(1)[1]
	q_sp = target_network(data.observations).detach()
	target_max = q_sp[
	np.arange(len(data.dones)), argmax_a].unsqueeze(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:

	prev = old_val.detach().cpu().numpy()
	new = td_target.detach().cpu().numpy()
	diff, a_diff = new-prev, np.abs(new-prev)

	mean, a_mean = np.mean(diff), np.mean(a_diff)
	median, a_median = np.median(diff), np.median(a_diff)
	maximum, a_maximum = np.max(diff), np.max(a_diff)
	minimum, a_minimum = np.min(diff), np.min(a_diff)
	std, a_std = np.std(diff), np.std(a_diff)
	below, a_below = mean - std, a_mean - a_std
	above, a_above = mean + std, a_mean + a_std
	pu_scalar, a_pu_scalar = 2 * mean / maximum, 2 * a_mean / a_maximum
	policy_frequency_scalar_ratio = 1.0 * pu_scalar
	a_policy_frequency_scalar_ratio = 1.0 * a_pu_scalar

	log_value("losses/td_loss", loss, global_step)
	log_value("losses/q_values", old_val.mean().item(), global_step)
	log_value("td/mean", mean, global_step)
	log_value("td/a_mean", a_mean, global_step)
	log_value("td/median", median, global_step)
	log_value("td/a_median", a_median, global_step)
	log_value("td/max", maximum, global_step)
	log_value("td/a_max", a_maximum, global_step)
	log_value("td/min", minimum, global_step)
	log_value("td/a_min", a_minimum, global_step)
	log_value("td/std", std, global_step)
	log_value("td/a_std", a_std, global_step)
	log_value("td/below", below, global_step)
	log_value("td/a_below", a_below, global_step)
	log_value("td/above", above, global_step)
	log_value("td/a_above", a_above, global_step)
	log_value("alg/pu_scalar", pu_scalar, global_step)
	log_value("alg/a_pu_scalar", a_pu_scalar, global_step)
	log_value("alg/policy_frequency_scalar_ratio", policy_frequency_scalar_ratio, global_step)
	log_value("alg/a_policy_frequency_scalar_ratio", a_policy_frequency_scalar_ratio, global_step)
	log_value("debug/steps_per_second", int(global_step / (time.time() - start_time)), global_step)

	# optimize the model
	optimizer.zero_grad()
	loss.backward()
	torch.nn.utils.clip_grad_norm_(q_network.parameters(),
	args.max_gradient_norm)
	optimizer.step()

	# update target network
	if global_step % args.target_network_frequency == 0:
	target_update_counter += 1
	for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()):
	target_network_param.data.copy_(
	args.target_tau * q_network_param.data + (1.0 - args.target_tau) * target_network_param.data
	)
	policy_update_counter += 1

	if global_step % 100 == 0:
	log_value("alg/n_target_update", target_update_counter, global_step)
	log_value("alg/n_policy_update", policy_update_counter, global_step)

	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):
	log_value("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, np.mean(episode_returns), repo_id, "DQN", f"runs/{run_name}", f"videos/{run_name}-eval")

	wandb_sess.finish()
	envs.close()
	writer.close()