pushing model

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Pong-base.cleanrl_model filter=lfs diff=lfs merge=lfs -text

Pong-base.cleanrl_model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4af4b7368797cb8c4e50097d1e16ef0004ded85e6303f1553c8c16905f19930b
+size 6751815

README.md

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+---
+tags:
+- PongNoFrameskip-v4
+- deep-reinforcement-learning
+- reinforcement-learning
+- custom-implementation
+library_name: cleanrl
+model-index:
+- name: DQN
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PongNoFrameskip-v4
+      type: PongNoFrameskip-v4
+    metrics:
+    - type: mean_reward
+      value: 18.80 +/- 1.25
+      name: mean_reward
+      verified: false
+---
+
+# (CleanRL) **DQN** Agent Playing **PongNoFrameskip-v4**
+
+This is a trained model of a DQN agent playing PongNoFrameskip-v4.
+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/dqn_atari.py).
+
+## Get Started
+
+To use this model, please install the `cleanrl` package with the following command:
+
+```
+pip install "cleanrl[dqn_atari]"
+python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id PongNoFrameskip-v4
+```
+
+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/odiaz1066/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/dqn_atari.py
+curl -OL https://huggingface.co/odiaz1066/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/pyproject.toml
+curl -OL https://huggingface.co/odiaz1066/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/poetry.lock
+poetry install --all-extras
+python dqn_atari.py --env-id PongNoFrameskip-v4 --track --save-model --capture-video --resume --total-timesteps 0 --upload-model --seed 1 --hf-entity odiaz1066
+```
+
+# Hyperparameters
+```python
+{'batch_size': 32,
+ 'buffer_size': 1000000,
+ 'capture_video': True,
+ 'checkpoint': False,
+ 'checkpoint_frequency': 100000,
+ 'cuda': True,
+ 'end_e': 0.01,
+ 'env_id': 'PongNoFrameskip-v4',
+ 'exp_name': 'dqn_atari',
+ 'exploration_fraction': 0.1,
+ 'gamma': 0.99,
+ 'hf_entity': 'odiaz1066',
+ 'initial_steps': 0,
+ 'learning_rate': 0.0001,
+ 'learning_starts': 80000,
+ 'num_envs': 1,
+ 'resume': True,
+ 'rotate': False,
+ 'save_model': True,
+ 'seed': 1,
+ 'start_e': 1,
+ 'target_network_frequency': 1000,
+ 'tau': 1.0,
+ 'torch_deterministic': True,
+ 'total_timesteps': 0,
+ 'track': True,
+ 'train_frequency': 4,
+ 'upload_model': True,
+ 'wandb_entity': None,
+ 'wandb_project_name': 'lagomorph'}
+```
+    

dqn_atari.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 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
+from typing import Callable
+from dotenv import load_dotenv
+import sys
+sys.path.append('.')
+from wrappers.rotation import RotateObservation
+
+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="lagomorph",
+        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")
+    parser.add_argument("--checkpoint", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to checkpoint the model")
+    parser.add_argument("--checkpoint-frequency", type=int, default=100000,
+        help="the frequency of saving checkpoint")
+    parser.add_argument("--resume", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="resume training from the last checkpoint")
+    parser.add_argument("--initial-steps", type=int, default=0,
+        help="the number of steps that were done in previous training runs")
+    parser.add_argument("--rotate", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to rotate the observation sometimes")
+    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)
+        if args.rotate:
+            env = RotateObservation(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)
+
+def evaluate(
+    model_path: str,
+    make_env: Callable,
+    env_id: str,
+    eval_episodes: int,
+    run_name: str,
+    Model: torch.nn.Module,
+    device: torch.device = torch.device("cpu"),
+    epsilon: float = 0.05,
+    capture_video: bool = True,
+):
+    envs = gym.vector.SyncVectorEnv([make_env(env_id, 0, 0, capture_video, run_name)])
+    model = Model(envs).to(device)
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model.eval()
+
+    obs, _ = envs.reset()
+    episodic_returns = []
+    while len(episodic_returns) < eval_episodes:
+        if random.random() < epsilon:
+            actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
+        else:
+            q_values = model(torch.Tensor(obs).to(device))
+            actions = torch.argmax(q_values, dim=1).cpu().numpy()
+        next_obs, _, _, _, infos = envs.step(actions)
+        if "final_info" in infos:
+            for info in infos["final_info"]:
+                if "episode" in info:
+                    print(f"eval_episode={len(episodic_returns)}, episodic_return={info['episode']['r']}")
+                    episodic_returns += [info["episode"]["r"]]
+        obs = next_obs
+
+    return episodic_returns
+
+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()
+    load_dotenv()
+    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()
+
+    if args.resume:
+        checkpoint_path = f"models/Pong.checkpoint.pth"
+        q_network.load_state_dict(torch.load(checkpoint_path))
+        print(f"checkpoint loaded from {checkpoint_path}")
+
+    # TRY NOT TO MODIFY: start the game
+    obs, _ = envs.reset(seed=args.seed)
+    for global_step in range(args.initial_steps, 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
+                    )
+            
+            # save checkpoint
+            if (args.checkpoint):
+                if global_step % args.checkpoint_frequency == 0:
+                    checkpoint_path = f"runs/{run_name}/{args.exp_name}.checkpoint.pth"
+                    torch.save(q_network.state_dict(), checkpoint_path)
+                    print(f"checkpoint saved to {checkpoint_path}")
+                    if args.track:
+                        artifact = wandb.Artifact(f"{args.exp_name}_checkpoint", "checkpoint")
+                        artifact.add_file(checkpoint_path)
+                        wandb.log_artifact(artifact)
+
+
+    if args.save_model:
+        model_path = f"runs/{run_name}/Pong-base.cleanrl_model"
+        torch.save(q_network.state_dict(), model_path)
+        print(f"model saved to {model_path}")
+        if args.track:
+            artifact = wandb.Artifact(f"Pong-base_model", "model")
+            artifact.add_file(model_path)
+            wandb.log_artifact(artifact)
+
+        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:
+            #TODO: Currently gives an error. *Maybe* I'll get around to fixing it once everything else works correctly.
+            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")
+
+    envs.close()
+    writer.close()

events.out.tfevents.1685407072.ccaa1a6bd80b.4247.0

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1082b650fa6317e84b963fb47d61d01be3fcb522b9177bc1b1799a8901a93c35
+size 1305

pyproject.toml

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+[tool.poetry]
+name = "lagomorph"
+version = "0.1.0"
+description = "Fork of CleanRL focused on DQN training"
+packages = [
+    { include = "envs" },
+    { include = "utils" },
+]
+keywords = ["reinforcement", "machine", "learning", "research"]
+license="MIT"
+readme = "README.md"