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).
