# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/dqn/#dqn_ataripy import argparse import os import random import time from distutils.util import strtobool import gymnasium as gym import numpy as np import sys import torch import torch.nn as nn import torch.nn.functional as F 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 sys.path.append('c:\\Users\\Retsal\\Documents\\ARF\\cleanrl') from cleanrl_utils.evals.dqn_eval import evaluate 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=1e-4, help="the learning rate of the optimizer") parser.add_argument("--num-envs", type=int, default=1, help="the number of parallel game environments") 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("--tau", type=float, default=1., help="the target network update rate") parser.add_argument("--target-network-frequency", type=int, default=1000, 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=0.10, 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 assert args.num_envs == 1, "vectorized envs are not supported at the moment" 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 = 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.action_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.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, env.single_action_space.n), ) def forward(self, x): return self.network(x / 255.0) 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" "gymnasium[atari,accept-rom-license]==0.28.1" "ale-py==0.8.1" """ ) 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 + 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, optimize_memory_usage=True, 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 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, terminated, truncated, infos = envs.step(actions) # TRY NOT TO MODIFY: record rewards for plotting purposes if "final_info" in infos: for info in infos["final_info"]: # Skip the envs that are not done if "episode" not in info: continue 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) # TRY NOT TO MODIFY: save data to reply buffer; handle `final_observation` real_next_obs = next_obs.copy() for idx, d in enumerate(truncated): if d: real_next_obs[idx] = infos["final_observation"][idx] rb.add(obs, real_next_obs, actions, rewards, terminated, 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) 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) 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 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): 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, "DQN", f"runs/{run_name}", f"videos/{run_name}-eval") envs.close() writer.close()