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import argparse |
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import os |
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import random |
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import time |
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from distutils.util import strtobool |
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import gymnasium as gym |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.optim as optim |
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from stable_baselines3.common.atari_wrappers import ( |
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ClipRewardEnv, |
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EpisodicLifeEnv, |
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FireResetEnv, |
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MaxAndSkipEnv, |
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NoopResetEnv |
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) |
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from stable_baselines3.common.buffers import ReplayBuffer |
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from torch.utils.tensorboard import SummaryWriter |
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def parse_args(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"), |
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help="the name of this experiment") |
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parser.add_argument("--seed", type=int, default=1, |
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help="seed of the experiment") |
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parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True, |
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help="if toggled, `torch.backends.cudnn.deterministic=False`") |
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parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True, |
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help="if toggled, cuda will be enabled by default") |
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parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True, |
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help="if toggled, this experiment will be tracked with Weights and Biases") |
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parser.add_argument("--wandb-project-name", type=str, default="cleanRL", |
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help="the wandb's project name") |
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parser.add_argument("--wandb-entity", type=str, default=None, |
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help="the entity (team) of wandb's project") |
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parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True, |
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help="whether to capture videos of the agent performances (check out `videos` folder)") |
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parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True, |
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help="whether to save model into the `runs/{run_name}` folder") |
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parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True, |
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help="whether to upload the saved model to huggingface") |
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parser.add_argument("--hf-entity", type=str, default="", |
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help="the user or org name of the model repository from the Hugging Face Hub") |
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parser.add_argument("--env-id", type=str, default="BreakoutNoFrameskip-v4", |
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help="the id of the environment") |
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parser.add_argument("--total-timesteps", type=int, default=100000, |
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help="total timesteps of the experiments") |
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parser.add_argument("--learning-rate", type=float, default=1e-4, |
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help="the learning rate of the optimizer") |
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parser.add_argument("--num-envs", type=int, default=1, |
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help="the number of parallel game environments") |
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parser.add_argument("--buffer-size", type=int, default=1000000, |
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help="the replay memory buffer size") |
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parser.add_argument("--gamma", type=float, default=0.99, |
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help="the discount factor gamma") |
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parser.add_argument("--tau", type=float, default=1., |
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help="the target network update rate") |
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parser.add_argument("--target-network-frequency", type=int, default=1000, |
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help="the timesteps it takes to update the target network") |
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parser.add_argument("--batch-size", type=int, default=32, |
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help="the batch size of sample from the reply memory") |
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parser.add_argument("--start-e", type=float, default=1, |
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help="the starting epsilon for exploration") |
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parser.add_argument("--end-e", type=float, default=0.01, |
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help="the ending epsilon for exploration") |
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parser.add_argument("--exploration-fraction", type=float, default=0.10, |
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help="the fraction of `total-timesteps` it takes from start-e to go end-e") |
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parser.add_argument("--learning-starts", type=int, default=80000, |
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help="timestep to start learning") |
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parser.add_argument("--train-frequency", type=int, default=4, |
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help="the frequency of training") |
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args = parser.parse_args() |
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assert args.num_envs == 1, "vectorized envs are not supported at the moment" |
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return args |
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def make_env(env_id, seed, idx, capture_video, run_name): |
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def thunk(): |
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if capture_video and idx == 0: |
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env = gym.make(env_id, render_mode="rgb_array") |
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env = gym.wrappers.RecordVideo(env, f"videos/{run_name}") |
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else: |
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env = gym.make(env_id) |
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env = gym.wrappers.RecordEpisodeStatistics(env) |
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env = NoopResetEnv(env, noop_max=30) |
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env = MaxAndSkipEnv(env, skip=4) |
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env = EpisodicLifeEnv(env) |
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if "FIRE" in env.unwrapped.get_action_meanings(): |
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env = FireResetEnv(env) |
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env = ClipRewardEnv(env) |
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env = gym.wrappers.ResizeObservation(env, (84, 84)) |
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env = gym.wrappers.GrayScaleObservation(env) |
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env = gym.wrappers.FrameStack(env, 4) |
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env.action_space.seed(seed) |
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return env |
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return thunk |
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class QNetwork(nn.Module): |
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def __init__(self, env): |
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super().__init__() |
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self.network = nn.Sequential( |
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nn.Conv2d(4, 32, 8, stride=4), |
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nn.ReLU(), |
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nn.Conv2d(32, 64, 4, stride=2), |
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nn.ReLU(), |
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nn.Conv2d(64, 64, 3, stride=1), |
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nn.ReLU(), |
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nn.Flatten(), |
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nn.Linear(3136, 512), |
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nn.ReLU(), |
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nn.Linear(512, env.single_action_space.n), |
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) |
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def forward(self, x): |
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return self.network(x / 255.0) |
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def linear_schedule(start_e: float, end_e: float, duration: int, t: int): |
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slope = (end_e - start_e) / duration |
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return max(slope * t + start_e, end_e) |
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if __name__ == "__main__": |
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import stable_baselines3 as sb3 |
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from huggingface_hub import login |
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from dotenv import load_dotenv, find_dotenv |
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load_dotenv(find_dotenv()) |
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HF_TOKEN = os.environ.get("HF_TOKEN") |
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login(HF_TOKEN) |
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if sb3.__version__ < "2.0": |
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raise ValueError( |
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"""On going migration: run the following command to install new dependencies |
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pip install "stable_baselines3==2.0.0a1" "gymnasium[atari,accept-rom-license]==0.28.1" "ale-py==0.8.1" |
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""" |
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) |
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args = parse_args() |
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run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}" |
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if args.track: |
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import wandb |
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wandb.init( |
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project=args.wandb_project_name, |
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entity=args.wandb_entity, |
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sync_tensorboard=True, |
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config=vars(args), |
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name=run_name, |
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monitor_gym=True, |
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save_code=True |
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) |
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writer = SummaryWriter(f"runs/{run_name}") |
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writer.add_text( |
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"hyperparameters", |
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"|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])), |
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) |
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random.seed(args.seed) |
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np.random.seed(args.seed) |
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torch.manual_seed(args.seed) |
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torch.backends.cudnn.deterministic = args.torch_deterministic |
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device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") |
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envs = gym.vector.SyncVectorEnv( |
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[make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)] |
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) |
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assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported" |
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q_network = QNetwork(envs).to(device) |
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optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate) |
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target_network = QNetwork(envs).to(device) |
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target_network.load_state_dict(q_network.state_dict()) |
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rb = ReplayBuffer( |
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args.buffer_size, |
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envs.single_observation_space, |
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envs.single_action_space, |
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device, |
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optimize_memory_usage=True, |
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handle_timeout_termination=False |
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) |
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start_time = time.time() |
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obs, _ = envs.reset(seed=args.seed) |
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for global_step in range(args.total_timesteps): |
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epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step) |
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if random.random() < epsilon: |
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actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)]) |
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else: |
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q_values = q_network(torch.Tensor(obs).to(device)) |
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actions = torch.argmax(q_values, dim=1).cpu().numpy() |
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next_obs, rewards, terminated, truncated, infos = envs.step(actions) |
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if "final_info" in infos: |
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for info in infos["final_info"]: |
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if "episode" not in info: |
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continue |
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print(f"global_step={global_step}, episodic_return={info['episode']['r']}") |
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writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step) |
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writer.add_scalar("charts/episode_length", info["episode"]["l"], global_step) |
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writer.add_scalar("charts/epsilon", epsilon, global_step) |
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real_next_obs = next_obs.copy() |
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for idx, d in enumerate(truncated): |
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if d: |
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real_next_obs[idx] = infos["final_observation"][idx] |
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rb.add(obs, real_next_obs, actions, rewards, terminated, infos) |
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obs = next_obs |
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if global_step > args.learning_starts: |
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if global_step % args.train_frequency == 0: |
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data = rb.sample(args.batch_size) |
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with torch.no_grad(): |
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target_max, _ = target_network(data.next_observations).max(dim=1) |
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td_target = data.rewards.flatten() + args.gamma * target_max * (1 - data.dones.flatten()) |
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old_val = q_network(data.observations).gather(1, data.actions).squeeze() |
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loss = F.mse_loss(td_target, old_val) |
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if global_step % 100 == 0: |
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writer.add_scalar("losses/td_loss", loss, global_step) |
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writer.add_scalar("losses/q_values", old_val.mean().item(), global_step) |
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print("SPS:", int(global_step / (time.time() - start_time))) |
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writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step) |
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optimizer.zero_grad() |
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loss.backward() |
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optimizer.step() |
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if global_step % args.target_network_frequency == 0: |
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for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()): |
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target_network_param.data.copy_( |
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args.tau * q_network_param.data + (1.0 - args.tau) * target_network_param.data |
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) |
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if args.save_model: |
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model_path = f"runs/{run_name}/{args.exp_name}.pth" |
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torch.save(q_network.state_dict(), model_path) |
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print(f"model saved to {model_path}") |
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from dqn_eval import evaluate |
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episodic_returns = evaluate( |
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model_path, |
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make_env, |
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args.env_id, |
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eval_episode=10, |
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run_name=f"{run_name}-eval", |
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Model=QNetwork, |
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device=device, |
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epsilon=0.05, |
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) |
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for idx, episodic_return in enumerate(episodic_returns): |
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writer.add_scalar("eval/episodic_return", episodic_return, idx) |
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if args.upload_model: |
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from huggingface import push_to_hub |
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repo_name = f"{args.exp_name}" |
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repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name |
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push_to_hub(args, episodic_returns, repo_id, "DQN", f"runs/{run_name}", f"videos/{run_name}-eval") |
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envs.close() |
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writer.close() |
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