---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-RL
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: Pixelcopter-PLE-v0
      type: Pixelcopter-PLE-v0
    metrics:
    - type: mean_reward
      value: 13.10 +/- 6.89
      name: mean_reward
      verified: false
---
# REINFORCE Agent for Pixelcopter-PLE-v0

## Model Description

This repository contains a trained REINFORCE (Policy Gradient) reinforcement learning agent that has learned to play Pixelcopter-PLE-v0, a challenging helicopter navigation game from the PyGame Learning Environment (PLE). The agent uses policy gradient methods to learn optimal flight control strategies through trial and error.

### Model Details

- **Algorithm**: REINFORCE (Monte Carlo Policy Gradient)
- **Environment**: Pixelcopter-PLE-v0 (PyGame Learning Environment)
- **Framework**: Custom implementation following Deep RL Course guidelines
- **Task Type**: Discrete Control (Binary Actions)
- **Action Space**: Discrete (2 actions: do nothing or thrust up)
- **Observation Space**: Visual/pixel-based or feature-based state representation

### Environment Overview

Pixelcopter-PLE-v0 is a classic helicopter control game where:
- **Objective**: Navigate a helicopter through obstacles without crashing
- **Challenge**: Requires precise timing and control to avoid ceiling, floor, and obstacles
- **Physics**: Gravity constantly pulls the helicopter down; player must apply thrust to maintain altitude
- **Scoring**: Points are awarded for surviving longer and successfully navigating through gaps
- **Difficulty**: Requires learning temporal dependencies and precise action timing

## Performance

The trained REINFORCE agent achieves the following performance metrics:

- **Mean Reward**: 13.10 ± 6.89
- **Performance Analysis**: This represents solid performance for this challenging environment
- **Consistency**: The standard deviation indicates moderate variability, which is expected for policy gradient methods


## Educational Resources

This model was developed following the **Deep Reinforcement Learning Course Unit 4**:
- **Course Link**: [https://huggingface.co/deep-rl-course/unit4/introduction](https://huggingface.co/deep-rl-course/unit4/introduction)
- **Topic**: Policy Gradient Methods and REINFORCE
- **Learning Objectives**: Understanding policy-based RL algorithms

For comprehensive learning about REINFORCE and policy gradient methods, refer to the complete course materials.