Qdagger

cleanrl/qdagger_dqn_atari_impalacnn.py

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+# 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 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.atari_wrappers import (
+    ClipRewardEnv,
+    EpisodicLifeEnv,
+    FireResetEnv,
+    MaxAndSkipEnv,
+    NoopResetEnv,
+)
+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="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("--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("--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
+    return args
+
+
+def make_env(env_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(env_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
+        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.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+
+    return thunk
+
+
+
+# taken from https://github.com/AIcrowd/neurips2020-procgen-starter-kit/blob/142d09586d2272a17f44481a115c4bd817cf6a94/models/impala_cnn_torch.py
+class ResidualBlock(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.conv0 = nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=3, padding=1)
+        self.conv1 = nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        inputs = x
+        x = nn.functional.relu(x)
+        x = self.conv0(x)
+        x = nn.functional.relu(x)
+        x = self.conv1(x)
+        return x + inputs
+
+
+class ConvSequence(nn.Module):
+    def __init__(self, input_shape, out_channels):
+        super().__init__()
+        self._input_shape = input_shape
+        self._out_channels = out_channels
+        print("input channels", self._input_shape[0])
+        self.conv = nn.Conv2d(in_channels=self._input_shape[0], out_channels=self._out_channels, kernel_size=3, padding=1)
+        self.res_block0 = ResidualBlock(self._out_channels)
+        self.res_block1 = ResidualBlock(self._out_channels)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = nn.functional.max_pool2d(x, kernel_size=3, stride=2, padding=1)
+        x = self.res_block0(x)
+        x = self.res_block1(x)
+        assert x.shape[1:] == self.get_output_shape()
+        return x
+
+    def get_output_shape(self):
+        _c, h, w = self._input_shape
+        return (self._out_channels, (h + 1) // 2, (w + 1) // 2)
+
+
+# ALGO LOGIC: initialize agent here:
+class QNetwork(nn.Module):
+    def __init__(self, env):
+        super().__init__()
+        c, h, w = envs.single_observation_space.shape
+        shape = (c, h, w)
+        print(shape)
+        conv_seqs = []
+        for out_channels in [16, 32, 32]:
+            conv_seq = ConvSequence(shape, out_channels)
+            shape = conv_seq.get_output_shape()
+            conv_seqs.append(conv_seq)
+        conv_seqs += [
+            nn.Flatten(),
+            nn.ReLU(),
+            nn.Linear(in_features=shape[0] * shape[1] * shape[2], out_features=256),
+            nn.ReLU(),
+            nn.Linear(in_features=256, out_features=env.single_action_space.n),
+        ]
+        self.network = nn.Sequential(*conv_seqs)
+
+    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__":
+    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.Discrete), "only discrete action space is supported"
+
+    q_network = QNetwork(envs).to(device)
+    from torchsummary import summary
+    summary(q_network, (4, 84, 84))
+
+    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=True,
+    )
+    start_time = time.time()
+
+    # TRY NOT TO MODIFY: start the game
+    obs = envs.reset()
+    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, dones, infos = envs.step(actions)
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        for info in infos:
+            if "episode" in info.keys():
+                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)
+                break
+
+        # TRY NOT TO MODIFY: save data to reply buffer; handle `terminal_observation`
+        real_next_obs = next_obs.copy()
+        for idx, d in enumerate(dones):
+            if d:
+                real_next_obs[idx] = infos[idx]["terminal_observation"]
+        rb.add(obs, real_next_obs, actions, rewards, dones, 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 the target network
+            if global_step % args.target_network_frequency == 0:
+                target_network.load_state_dict(q_network.state_dict())
+
+    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()