---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1-policy-gradient-RL
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: CartPole-v1
      type: CartPole-v1
    metrics:
    - type: mean_reward
      value: 500.00 +/- 0.00
      name: mean_reward
      verified: false
---
# CartPole-v1 Policy Gradient Reinforcement Learning Model

## Model Description

This model is a Policy Gradient (REINFORCE) agent trained to solve the CartPole-v1 environment from OpenAI Gym. The agent learns to balance a pole on a cart by taking discrete actions (left or right) to maximize the cumulative reward.

## Model Details

### Model Architecture
- **Algorithm**: REINFORCE (Monte Carlo Policy Gradient)
- **Neural Network**: Simple feedforward network
  - Hidden layer size: 16 units
  - Activation function: ReLU (typical for policy networks)
  - Output layer: Softmax for action probabilities

### Training Configuration
- **Environment**: CartPole-v1 (OpenAI Gym)
- **Training Episodes**: 2,000
- **Max Steps per Episode**: 1,000
- **Learning Rate**: 0.01
- **Discount Factor (γ)**: 1.0 (no discounting)
- **Optimizer**: Adam (PyTorch default)

## Environment Details

**CartPole-v1** is a classic control problem where:
- **Observation Space**: 4-dimensional continuous space
  - Cart position: [-4.8, 4.8]
  - Cart velocity: [-∞, ∞]
  - Pole angle: [-0.418 rad, 0.418 rad]
  - Pole angular velocity: [-∞, ∞]
- **Action Space**: 2 discrete actions (0: push left, 1: push right)
- **Reward**: +1 for every step the pole remains upright
- **Episode Termination**: 
  - Pole angle > ±12°
  - Cart position > ±2.4
  - Episode length > 500 steps (CartPole-v1 limit)

## Training Process

The model was trained using the REINFORCE algorithm with the following key features:

1. **Return Calculation**: Monte Carlo returns computed using dynamic programming for efficiency
2. **Reward Standardization**: Returns are normalized (zero mean, unit variance) for training stability
3. **Policy Loss**: Negative log-probability weighted by standardized returns
4. **Gradient Update**: Standard backpropagation with Adam optimizer

### Key Implementation Details
- Returns calculated in reverse chronological order for computational efficiency
- Numerical stability ensured by adding epsilon to standard deviation
- Deque data structure used for efficient O(1) operations

## Performance

The model is evaluated over 10 episodes after training. Expected performance:
- **Target**: Consistently achieve scores close to 500 (maximum possible in CartPole-v1)
- **Success Criterion**: Average score > 475 over evaluation episodes
- **Training Stability**: 100-episode rolling average tracked during training

## Usage

```python
# Load the trained policy
policy = torch.load('policy_model.pth')

# Use the policy to select actions
state = env.reset()
action, log_prob = policy.act(state)
```

## Limitations and Considerations

1. **Environment Specific**: Model is specifically trained for CartPole-v1 and won't generalize to other environments
2. **Sample Efficiency**: REINFORCE can be sample inefficient compared to modern policy gradient methods
3. **Variance**: High variance in policy gradient estimates (not using baseline/critic)
4. **Hyperparameter Sensitivity**: Performance may be sensitive to learning rate and network architecture

## Ethical Considerations

This is a simple control task with no ethical implications. The model is designed for:
- Educational purposes in reinforcement learning
- Benchmarking and algorithm development
- Research in policy gradient methods

## Training Environment

- **Framework**: PyTorch
- **Environment**: OpenAI Gym
- **Monitoring**: 100-episode rolling average for performance tracking

## Model Files

- `policy_model.pth`: Trained policy network weights
- `training_scores.pkl`: Training episode scores for analysis

## Citation

If you use this model, please cite:

```bibtex
@misc{cartpole-policy-gradient-2024,
  title={CartPole-v1 Policy Gradient Reinforcement Learning Model},
  author={Adilbai},
  year={2024},
  publisher={Hugging Face Hub},
  url={https://huggingface.co/Adilbai/CartPole-v1-policy-gradient-RL}
}
```

## References

- Sutton, R. S., & Barto, A. G. (2018). *Reinforcement learning: An introduction*. MIT press.
- Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. *Machine learning*, 8(3-4), 229-256.
- OpenAI Gym CartPole-v1 Environment Documentation

---

*For questions or issues with this model, please open an issue in the repository.*