Corgi Game Reinforcement Learning

This video shows the trained RL agent successfully navigating the game environment.

Key Learnings

  • Practical implementation of reinforcement learning algorithms
  • Different RL algorithms and their capabilities
  • Value of iterating on reward function design and state space design
  • Unity ML-Agents framework capabilities

Project Overview

I created a reinforcement learning agent to play "Run, Corgi, Run!" in a custom Unity environment. This project helped me understand the practical challenges of implementing RL algorithms and the importance of state space design. Project scope included: - Integrated ML-Agents with a 2D game in Unity to create a custom environment and feed information to the agent - Debugged issues with the game code and environment feeding incorrect information to the agent - Implemented custom reward system to encourage desired agent behavior - Trained the agent on a cloud server, comparing both PPO and SAC algorithms - Experimented with different hyperparameter configurations to optimize learning

Technical Details

- Utilized GPU compute on server to train the agent quickly - Monitored training progress with TensorBoard and adjusted parameters for optimal performance - Considered curriculum learning to overcome game complexity

Technologies Used

Unity ML-Agents PPO SAC Reinforcement Learning C# TensorBoard