taherfattahi/ppo-rocket-landing
📦 Open Source Projecttaherfattahi
A clean PyTorch implementation of PPO for mastering complex rocket landing maneuvers in custom environments.
The ppo-rocket-landing repository serves as a practical implementation of the Proximal Policy Optimization (PPO) algorithm, a popular policy gradient method in reinforcement learning. The project focuses on the challenging task of rocket landing, requiring the agent to manage continuous action spaces, velocity control, and landing stability. Technically, the implementation utilizes PyTorch to define the actor-critic architecture, allowing for efficient gradient updates and stable policy convergence. The custom environment provides a controlled sandbox where users can experiment with different reward functions and hyperparameter configurations. Key features include a modular structure that separates the environment logic from the training loop, clear implementation of the PPO clip objective, and support for GPU acceleration. This project is an excellent resource for those looking to understand how to bridge the gap between theoretical RL algorithms and real-world control tasks, providing a baseline for more complex aerospace simulation projects.
💡Highlights
- ├─Custom PyTorch PPO implementation
- ├─Physics-based rocket simulation
- └─Continuous action space control
🎯For
- ├─Reinforcement Learning Researchers
- ├─Robotics Engineers
- └─AI Students