""" *Filename :dqn.py *Description : *Time :2024/11/13 18:34:33 *Author :jackson *Version :1.0 """ import os import random import time from dataclasses import dataclass 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 import tyro from stable_baselines3.common.buffers import ReplayBuffer from torch.utils.tensorboard import SummaryWriter @dataclass class Args: exp_name: str = os.path.basename(__file__)[: -len(".py")] """the name of this experiment""" seed: int = 1 """seed of the experiment""" torch_deterministic: bool = True """if toggled, `torch.backends.cudnn.deterministic=False`""" cuda: bool = True """if toggled, cuda will be enabled by default""" track: bool = False """if toggled, this experiment will be tracked with Weights and Biases""" wandb_project_name: str = "cleanRL" """the wandb's project name""" wandb_entity: str = None """the entity (team) of wandb's project""" capture_video: bool = False """whether to capture videos of the agent performances (check out `videos` folder)""" save_model: bool = False """whether to save model into the `runs/{run_name}` folder""" upload_model: bool = False """whether to upload the saved model to huggingface""" hf_entity: str = "jacksonhack" """the user or org name of the model repository from the Hugging Face Hub""" # Algorithm specific arguments env_id: str = "CartPole-v1" """the id of the environment""" total_timesteps: int = 500000 """total timesteps of the experiments""" learning_rate: float = 2.5e-4 """the learning rate of the optimizer""" num_envs: int = 1 """the number of parallel game environments""" buffer_size: int = 10000 """the replay memory buffer size""" gamma: float = 0.99 """the discount factor gamma""" tau: float = 1.0 """the target network update rate""" target_network_frequency: int = 500 """the timesteps it takes to update the target network""" batch_size: int = 128 """the batch size of sample from the reply memory""" start_e: float = 1 """the starting epsilon for exploration""" end_e: float = 0.05 """the ending epsilon for exploration""" exploration_fraction: float = 0.5 """the fraction of `total-timesteps` it takes from start-e to go end-e""" learning_starts: int = 10000 """timestep to start learning""" train_frequency: int = 10 """the frequency of training""" 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 class QNetwork(nn.Module): def __init__(self, env): super().__init__() self.network = nn.Sequential( nn.Linear(np.array(env.single_observation_space.shape).prod(), 120), nn.ReLU(), nn.Linear(120, 84), nn.ReLU(), nn.Linear(84, 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__": 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 = tyro.cli(Args) assert args.num_envs == 1, "vectorized envs are not supported at the moment" 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, handle_timeout_termination=False, ) start_time = time.time() 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, 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"]: if info and "episode" in 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) # TRY NOT TO MODIFY: save data to reply buffer; handle `final_observation` # 向量化环境会自己重置环境 real_next_obs = next_obs.copy() for idx, trunc in enumerate(truncations): if trunc: # 将截断状态变为真实状态，确保算法获得更准确的信息 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: 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) # tensor.max() 返回最大值及其索引 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) # SPS: Step per second 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 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 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")