Ant-v4-sac_continuous_action-seed5 / sac_continuous_action.py

pushing model

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	# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/sac/#sac_continuous_actionpy
	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.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="Hopper-v4",
	help="the id of the environment")
	parser.add_argument("--total-timesteps", type=int, default=1000000,
	help="total timesteps of the experiments")
	parser.add_argument("--buffer-size", type=int, default=int(1e6),
	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=0.005,
	help="target smoothing coefficient (default: 0.005)")
	parser.add_argument("--batch-size", type=int, default=256,
	help="the batch size of sample from the reply memory")
	parser.add_argument("--learning-starts", type=int, default=5e3,
	help="timestep to start learning")
	parser.add_argument("--policy-lr", type=float, default=3e-4,
	help="the learning rate of the policy network optimizer")
	parser.add_argument("--q-lr", type=float, default=1e-3,
	help="the learning rate of the Q network network optimizer")
	parser.add_argument("--policy-frequency", type=int, default=2,
	help="the frequency of training policy (delayed)")
	parser.add_argument("--target-network-frequency", type=int, default=1, # Denis Yarats' implementation delays this by 2.
	help="the frequency of updates for the target nerworks")
	parser.add_argument("--noise-clip", type=float, default=0.5,
	help="noise clip parameter of the Target Policy Smoothing Regularization")
	parser.add_argument("--alpha", type=float, default=0.2,
	help="Entropy regularization coefficient.")
	parser.add_argument("--autotune", type=lambda x:bool(strtobool(x)), default=True, nargs="?", const=True,
	help="automatic tuning of the entropy coefficient")
	args = parser.parse_args()
	# fmt: on
	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.action_space.seed(seed)
	return env

	return thunk


	# ALGO LOGIC: initialize agent here:
	class SoftQNetwork(nn.Module):
	def __init__(self, env):
	super().__init__()
	self.fc1 = nn.Linear(np.array(env.single_observation_space.shape).prod() + np.prod(env.single_action_space.shape), 256)
	self.fc2 = nn.Linear(256, 256)
	self.fc3 = nn.Linear(256, 1)

	def forward(self, x, a):
	x = torch.cat([x, a], 1)
	x = F.relu(self.fc1(x))
	x = F.relu(self.fc2(x))
	x = self.fc3(x)
	return x


	LOG_STD_MAX = 2
	LOG_STD_MIN = -5


	class Actor(nn.Module):
	def __init__(self, env):
	super().__init__()
	self.fc1 = nn.Linear(np.array(env.single_observation_space.shape).prod(), 256)
	self.fc2 = nn.Linear(256, 256)
	self.fc_mean = nn.Linear(256, np.prod(env.single_action_space.shape))
	self.fc_logstd = nn.Linear(256, np.prod(env.single_action_space.shape))
	# action rescaling
	self.register_buffer(
	"action_scale", torch.tensor((env.action_space.high - env.action_space.low) / 2.0, dtype=torch.float32)
	)
	self.register_buffer(
	"action_bias", torch.tensor((env.action_space.high + env.action_space.low) / 2.0, dtype=torch.float32)
	)

	def forward(self, x):
	x = F.relu(self.fc1(x))
	x = F.relu(self.fc2(x))
	mean = self.fc_mean(x)
	log_std = self.fc_logstd(x)
	log_std = torch.tanh(log_std)
	log_std = LOG_STD_MIN + 0.5 * (LOG_STD_MAX - LOG_STD_MIN) * (log_std + 1) # From SpinUp / Denis Yarats

	return mean, log_std

	def get_action(self, x):
	mean, log_std = self(x)
	std = log_std.exp()
	normal = torch.distributions.Normal(mean, std)
	x_t = normal.rsample() # for reparameterization trick (mean + std * N(0,1))
	y_t = torch.tanh(x_t)
	action = y_t * self.action_scale + self.action_bias
	log_prob = normal.log_prob(x_t)
	# Enforcing Action Bound
	log_prob -= torch.log(self.action_scale * (1 - y_t.pow(2)) + 1e-6)
	log_prob = log_prob.sum(1, keepdim=True)
	mean = torch.tanh(mean) * self.action_scale + self.action_bias
	return action, log_prob, mean


	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"
	"""
	)

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

	max_action = float(envs.single_action_space.high[0])

	actor = Actor(envs).to(device)
	qf1 = SoftQNetwork(envs).to(device)
	qf2 = SoftQNetwork(envs).to(device)
	qf1_target = SoftQNetwork(envs).to(device)
	qf2_target = SoftQNetwork(envs).to(device)
	qf1_target.load_state_dict(qf1.state_dict())
	qf2_target.load_state_dict(qf2.state_dict())
	q_optimizer = optim.Adam(list(qf1.parameters()) + list(qf2.parameters()), lr=args.q_lr)
	actor_optimizer = optim.Adam(list(actor.parameters()), lr=args.policy_lr)

	# Automatic entropy tuning
	if args.autotune:
	target_entropy = -torch.prod(torch.Tensor(envs.single_action_space.shape).to(device)).item()
	log_alpha = torch.zeros(1, requires_grad=True, device=device)
	alpha = log_alpha.exp().item()
	a_optimizer = optim.Adam([log_alpha], lr=args.q_lr)
	else:
	alpha = args.alpha

	envs.single_observation_space.dtype = np.float32
	rb = ReplayBuffer(
	args.buffer_size,
	envs.single_observation_space,
	envs.single_action_space,
	device,
	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
	if global_step < args.learning_starts:
	actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
	else:
	actions, _, _ = actor.get_action(torch.Tensor(obs).to(device))
	actions = actions.detach().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"]:
	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)
	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:
	data = rb.sample(args.batch_size)
	with torch.no_grad():
	next_state_actions, next_state_log_pi, _ = actor.get_action(data.next_observations)
	qf1_next_target = qf1_target(data.next_observations, next_state_actions)
	qf2_next_target = qf2_target(data.next_observations, next_state_actions)
	min_qf_next_target = torch.min(qf1_next_target, qf2_next_target) - alpha * next_state_log_pi
	next_q_value = data.rewards.flatten() + (1 - data.dones.flatten()) * args.gamma * (min_qf_next_target).view(-1)

	qf1_a_values = qf1(data.observations, data.actions).view(-1)
	qf2_a_values = qf2(data.observations, data.actions).view(-1)
	qf1_loss = F.mse_loss(qf1_a_values, next_q_value)
	qf2_loss = F.mse_loss(qf2_a_values, next_q_value)
	qf_loss = qf1_loss + qf2_loss

	# optimize the model
	q_optimizer.zero_grad()
	qf_loss.backward()
	q_optimizer.step()

	if global_step % args.policy_frequency == 0: # TD 3 Delayed update support
	for _ in range(
	args.policy_frequency
	): # compensate for the delay by doing 'actor_update_interval' instead of 1
	pi, log_pi, _ = actor.get_action(data.observations)
	qf1_pi = qf1(data.observations, pi)
	qf2_pi = qf2(data.observations, pi)
	min_qf_pi = torch.min(qf1_pi, qf2_pi)
	actor_loss = ((alpha * log_pi) - min_qf_pi).mean()

	actor_optimizer.zero_grad()
	actor_loss.backward()
	actor_optimizer.step()

	if args.autotune:
	with torch.no_grad():
	_, log_pi, _ = actor.get_action(data.observations)
	alpha_loss = (-log_alpha.exp() * (log_pi + target_entropy)).mean()

	a_optimizer.zero_grad()
	alpha_loss.backward()
	a_optimizer.step()
	alpha = log_alpha.exp().item()

	# update the target networks
	if global_step % args.target_network_frequency == 0:
	for param, target_param in zip(qf1.parameters(), qf1_target.parameters()):
	target_param.data.copy_(args.tau * param.data + (1 - args.tau) * target_param.data)
	for param, target_param in zip(qf2.parameters(), qf2_target.parameters()):
	target_param.data.copy_(args.tau * param.data + (1 - args.tau) * target_param.data)

	if global_step % 100 == 0:
	writer.add_scalar("losses/qf1_values", qf1_a_values.mean().item(), global_step)
	writer.add_scalar("losses/qf2_values", qf2_a_values.mean().item(), global_step)
	writer.add_scalar("losses/qf1_loss", qf1_loss.item(), global_step)
	writer.add_scalar("losses/qf2_loss", qf2_loss.item(), global_step)
	writer.add_scalar("losses/qf_loss", qf_loss.item() / 2.0, global_step)
	writer.add_scalar("losses/actor_loss", actor_loss.item(), global_step)
	writer.add_scalar("losses/alpha", alpha, 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)
	if args.autotune:
	writer.add_scalar("losses/alpha_loss", alpha_loss.item(), global_step)

	if args.save_model:
	model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
	torch.save((actor.state_dict(), qf1.state_dict(), qf2.state_dict()), model_path)
	print(f"model saved to {model_path}")
	from cleanrl_utils.evals.sac_eval import evaluate

	episodic_returns = evaluate(
	model_path,
	make_env,
	args.env_id,
	eval_episodes=10,
	run_name=f"{run_name}-eval",
	Model=(Actor, SoftQNetwork),
	device=device,
	)
	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, "SAC", f"runs/{run_name}", f"videos/{run_name}-eval")

	envs.close()
	writer.close()