pushing model

Qbert-v5.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:494b692b2d8ee39f4e5839ce035f20068fae68e1d5ce41624a1719e5336d7cdd
+size 6752403

README.md

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+---
+tags:
+- ALE/Qbert-v5
+- deep-reinforcement-learning
+- reinforcement-learning
+- custom-implementation
+library_name: cleanrl
+model-index:
+- name: DQN
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: ALE/Qbert-v5
+      type: ALE/Qbert-v5
+    metrics:
+    - type: mean_reward
+      value: 4122.50 +/- 314.73
+      name: mean_reward
+      verified: false
+---
+
+# (CleanRL) **DQN** Agent Playing **ALE/Qbert-v5**
+
+This is a trained model of a DQN agent playing ALE/Qbert-v5.
+The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
+found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/Qbert-v5.py).
+
+## Get Started
+
+To use this model, please install the `cleanrl` package with the following command:
+
+```
+pip install "cleanrl[Qbert-v5]"
+python -m cleanrl_utils.enjoy --exp-name Qbert-v5 --env-id ALE/Qbert-v5
+```
+
+Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
+
+
+## Command to reproduce the training
+
+```bash
+curl -OL https://huggingface.co/adhisetiawan/Qbert-v5/raw/main/dqn_atari.py
+curl -OL https://huggingface.co/adhisetiawan/Qbert-v5/raw/main/pyproject.toml
+curl -OL https://huggingface.co/adhisetiawan/Qbert-v5/raw/main/poetry.lock
+poetry install --all-extras
+python dqn_atari.py --exp-name Qbert-v5 --track --wandb-project-name ALE --capture-video --env-id ALE/Qbert-v5 --total-timesteps 1000000 --buffer-size 400000 --save-model --upload-model --hf-entity adhisetiawan
+```
+
+# Hyperparameters
+```python
+{'batch_size': 32,
+ 'buffer_size': 400000,
+ 'capture_video': True,
+ 'cuda': True,
+ 'end_e': 0.01,
+ 'env_id': 'ALE/Qbert-v5',
+ 'exp_name': 'Qbert-v5',
+ 'exploration_fraction': 0.1,
+ 'gamma': 0.99,
+ 'hf_entity': 'adhisetiawan',
+ 'learning_rate': 0.0001,
+ 'learning_starts': 80000,
+ 'num_envs': 1,
+ 'save_model': True,
+ 'seed': 1,
+ 'start_e': 1,
+ 'target_network_frequency': 1000,
+ 'tau': 1.0,
+ 'torch_deterministic': True,
+ 'total_timesteps': 1000000,
+ 'track': True,
+ 'train_frequency': 4,
+ 'upload_model': True,
+ 'wandb_entity': None,
+ 'wandb_project_name': 'ALE'}
+```
+    

dqn_atari.py

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+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.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("--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
+
+
+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(
+            """On going migration: run the following command to install new dependencies
+        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()])),
+    )
+
+    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")
+
+    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()
+
+    obs, _ = envs.reset(seed=args.seed)
+    for global_step in range(args.total_timesteps):
+        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()
+
+        next_obs, rewards, terminated, truncated, infos = envs.step(actions)
+
+        if "final_info" in infos:
+            for info in infos["final_info"]:
+                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/episode_length", info["episode"]["l"], global_step)
+                writer.add_scalar("charts/epsilon", epsilon, global_step)
+
+        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)
+
+        obs = next_obs
+
+        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)
+
+                optimizer.zero_grad()
+                loss.backward()
+                optimizer.step()
+
+            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}.pth"
+        torch.save(q_network.state_dict(), model_path)
+        print(f"model saved to {model_path}")
+
+        from dqn_eval import evaluate
+
+        episodic_returns = evaluate(
+            model_path,
+            make_env,
+            args.env_id,
+            eval_episode=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 huggingface import push_to_hub
+
+            repo_name = f"{args.exp_name}"
+            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()
+    

events.out.tfevents.1693372331.LAPTOP-9SN8UL2M.15381.0

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+size 2545882