HalfCheetah-v4-td3_continuous_action-seed3 / td3_continuous_action.py
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# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/td3/#td3_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("--learning-rate", type=float, default=3e-4,
help="the learning rate of the optimizer")
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("--policy-noise", type=float, default=0.2,
help="the scale of policy noise")
parser.add_argument("--exploration-noise", type=float, default=0.1,
help="the scale of exploration noise")
parser.add_argument("--learning-starts", type=int, default=25e3,
help="timestep to start learning")
parser.add_argument("--policy-frequency", type=int, default=2,
help="the frequency of training policy (delayed)")
parser.add_argument("--noise-clip", type=float, default=0.5,
help="noise clip parameter of the Target Policy Smoothing Regularization")
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 QNetwork(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
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_mu = 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))
x = torch.tanh(self.fc_mu(x))
return x * self.action_scale + self.action_bias
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"
actor = Actor(envs).to(device)
qf1 = QNetwork(envs).to(device)
qf2 = QNetwork(envs).to(device)
qf1_target = QNetwork(envs).to(device)
qf2_target = QNetwork(envs).to(device)
target_actor = Actor(envs).to(device)
target_actor.load_state_dict(actor.state_dict())
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.learning_rate)
actor_optimizer = optim.Adam(list(actor.parameters()), lr=args.learning_rate)
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:
with torch.no_grad():
actions = actor(torch.Tensor(obs).to(device))
actions += torch.normal(0, actor.action_scale * args.exploration_noise)
actions = actions.cpu().numpy().clip(envs.single_action_space.low, envs.single_action_space.high)
# 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():
clipped_noise = (torch.randn_like(data.actions, device=device) * args.policy_noise).clamp(
-args.noise_clip, args.noise_clip
) * target_actor.action_scale
next_state_actions = (target_actor(data.next_observations) + clipped_noise).clamp(
envs.single_action_space.low[0], envs.single_action_space.high[0]
)
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)
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:
actor_loss = -qf1(data.observations, actor(data.observations)).mean()
actor_optimizer.zero_grad()
actor_loss.backward()
actor_optimizer.step()
# update the target network
for param, target_param in zip(actor.parameters(), target_actor.parameters()):
target_param.data.copy_(args.tau * param.data + (1 - args.tau) * target_param.data)
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)
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.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.td3_eval import evaluate
episodic_returns = evaluate(
model_path,
make_env,
args.env_id,
eval_episodes=10,
run_name=f"{run_name}-eval",
Model=(Actor, QNetwork),
device=device,
exploration_noise=args.exploration_noise,
)
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, "TD3", f"runs/{run_name}", f"videos/{run_name}-eval")
envs.close()
writer.close()