Lunar Navigator is a reinforcement learning project that explores the dynamics of landing a spacecraft on the lunar surface. Using the Gymnasium library and Stable Baselines3, this project aims to develop an agent capable of safely navigating the challenges of lunar landing.
- Understanding Reinforcement Learning: This project serves as an introductory exploration into the principles of reinforcement learning (RL).
- Agent Development: To create an intelligent agent that learns to land on the moon by iterating through its training environment.
- Environment Setup: Implementation of the lunar lander environment provided by Gymnasium.
- Model Training: Utilization of Stable Baselines3 to train the agent over 10,000 iterations.
- Exploration vs. Exploitation: Addressing the critical balance during training to optimize the agent’s landing performance.
- Python 3.x
- Gymnasium
- Stable Baselines3
- NumPy