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	# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/c51/#c51_ataripy
	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.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=2.5e-4,
	help="the learning rate of the optimizer")
	parser.add_argument("--n-atoms", type=int, default=51,
	help="the number of atoms")
	parser.add_argument("--v-min", type=float, default=-10,
	help="the number of atoms")
	parser.add_argument("--v-max", type=float, default=10,
	help="the number of atoms")
	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("--target-network-frequency", type=int, default=10000,
	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=0.1,
	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
	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 = 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.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, n_atoms=101, v_min=-100, v_max=100):
	super().__init__()
	self.env = env
	self.n_atoms = n_atoms
	self.register_buffer("atoms", torch.linspace(v_min, v_max, steps=n_atoms))
	self.n = env.single_action_space.n
	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, self.n * n_atoms),
	)

	def get_action(self, x, action=None):
	logits = self.network(x / 255.0)
	# probability mass function for each action
	pmfs = torch.softmax(logits.view(len(x), self.n, self.n_atoms), dim=2)
	q_values = (pmfs * self.atoms).sum(2)
	if action is None:
	action = torch.argmax(q_values, 1)
	return action, pmfs[torch.arange(len(x)), action]


	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())}"
	run_name = "PongNoFrameskip-v4__c51_atari__1__1672771568"
	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, 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, n_atoms=args.n_atoms, v_min=args.v_min, v_max=args.v_max).to(device)
	optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate, eps=0.01 / args.batch_size)
	target_network = QNetwork(envs, n_atoms=args.n_atoms, v_min=args.v_min, v_max=args.v_max).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()

	# # 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:
	# actions, pmf = q_network.get_action(torch.Tensor(obs).to(device))
	# actions = actions.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():
	# 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 `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():
	# _, next_pmfs = target_network.get_action(data.next_observations)
	# next_atoms = data.rewards + args.gamma * target_network.atoms * (1 - data.dones)
	# # projection
	# delta_z = target_network.atoms[1] - target_network.atoms[0]
	# tz = next_atoms.clamp(args.v_min, args.v_max)

	# b = (tz - args.v_min) / delta_z
	# l = b.floor().clamp(0, args.n_atoms - 1)
	# u = b.ceil().clamp(0, args.n_atoms - 1)
	# # (l == u).float() handles the case where bj is exactly an integer
	# # example bj = 1, then the upper ceiling should be uj= 2, and lj= 1
	# d_m_l = (u + (l == u).float() - b) * next_pmfs
	# d_m_u = (b - l) * next_pmfs
	# target_pmfs = torch.zeros_like(next_pmfs)
	# for i in range(target_pmfs.size(0)):
	# target_pmfs[i].index_add_(0, l[i].long(), d_m_l[i])
	# target_pmfs[i].index_add_(0, u[i].long(), d_m_u[i])

	# _, old_pmfs = q_network.get_action(data.observations, data.actions.flatten())
	# loss = (-(target_pmfs * old_pmfs.clamp(min=1e-5, max=1 - 1e-5).log()).sum(-1)).mean()

	# if global_step % 100 == 0:
	# writer.add_scalar("losses/loss", loss.item(), global_step)
	# old_val = (old_pmfs * q_network.atoms).sum(1)
	# 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 the target network
	# if global_step % args.target_network_frequency == 0:
	# target_network.load_state_dict(q_network.state_dict())

	if args.save_model:
	model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
	# model_data = {
	# "model_weights": q_network.state_dict(),
	# "args": vars(args),
	# }
	# torch.save(model_data, model_path)
	print(f"model saved to {model_path}")
	from cleanrl_utils.evals.c51_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, "C51", f"runs/{run_name}", f"videos/{run_name}-eval")

	envs.close()
	writer.close()