Reward_Rush_DQN_Cart_Pole

Reward Rush: CartPole DQN

This repository contains the cleaned weights for a Deep Q-Network agent trained for the CartPole-v1 environment.

Model Architecture

The model utilizes a multi-layer perceptron structure designed for low-latency inference:

• Input: 4 state observations
• Output: 2 discrete actions
• Network Structure:
  • Linear(4, 128) -> ReLU
  • Linear(128, 128) -> ReLU
  • Linear(128, 2)

Common Implementation Mistakes to Avoid

1. Variable Naming: The weights are mapped to specific names: layer1, layer2, and layer3. Using generic names like fc1 or nn.Sequential will result in a loading error.
2. Missing Batch Dimension: The model expects a batch dimension. Input states must be wrapped using unsqueeze(0) before inference.
3. Inference Logic: The model outputs raw Q-values for both actions. Use argmax(dim=1) to select the correct action index for the environment.

Download and Test Code

This script downloads the weights from the Hugging Face repository, initializes the environment, and evaluates the agent over 100 test episodes.

import torch
import torch.nn as nn
import gymnasium as gym
import numpy as np
from huggingface_hub import hf_hub_download

class MatchedNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = nn.Linear(4, 128)
        self.layer2 = nn.Linear(128, 128)
        self.layer3 = nn.Linear(128, 2)

    def forward(self, x):
        x = torch.relu(self.layer1(x))
        x = torch.relu(self.layer2(x))
        return self.layer3(x)

def run_cartpole_test():
    path = hf_hub_download(repo_id="Nharen/Reward_Rush_DQN_Cart_Pole", filename="Cartpole.pth")
    
    model = MatchedNet()
    state_dict = torch.load(path, map_location='cpu', weights_only=True)
    
    if isinstance(state_dict, dict) and "policy_net_state_dict" in state_dict:
        state_dict = state_dict["policy_net_state_dict"]

    model.load_state_dict(state_dict)
    model.eval()

    env = gym.make("CartPole-v1")
    total_rewards = []

    for _ in range(100):
        state, _ = env.reset()
        episode_reward = 0
        done = False
        while not done:
            state_t = torch.as_tensor(state, dtype=torch.float32).unsqueeze(0)
            with torch.no_grad():
                action = model(state_t).argmax(dim=1).item()
            state, reward, terminated, truncated, _ = env.step(action)
            episode_reward += reward
            done = terminated or truncated
        total_rewards.append(episode_reward)

    print(f"Average Reward: {np.mean(total_rewards)}")
    env.close()

if __name__ == "__main__":
    run_cartpole_test()