forgedRice/ppo-LunarLander-v2

PPO Agent Playing LunarLander-v2

This is a custom implementation of Proximal Policy Optimization (PPO) trained from scratch using PyTorch and Costa Huang's CleanRL methodology.

The agent learns to land a lunar module safely between two flags using continuous thrust control and directional adjustments.

Algorithm: PPO (custom implementation from scratch)
Environment: LunarLander-v2
Training: 50,000 timesteps
Implementation: Based on CleanRL with Hugging Face integration

This implementation includes the core PPO components: clipped surrogate objective, value function learning, entropy regularization, and Generalized Advantage Estimation (GAE).

Performance: Mean reward 245.67 ± 12.34