ppo-rocket-landing
Basic Information
This repository implements the Proximal Policy Optimization (PPO) reinforcement learning algorithm using PyTorch to train an agent for a rocket landing task in a custom environment. It focuses on providing a practical implementation to define the environment, the PPO policy and training loop, and to run training experiments aimed at learning landing control. The project is intended as an experimental and instructional resource for those studying applied reinforcement learning on a physics-based control problem or adapting the implementation to other continuous control scenarios. The main purpose is to demonstrate and enable training and evaluation of a PPO agent on a rocket landing task.
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App Details
Features
PyTorch-based implementation of the PPO algorithm tailored for a rocket landing control problem. A custom environment that models the rocket landing task for use in training and evaluation. Training loop and policy code that apply PPO updates to learn control behaviors. Components organized to allow inspection and modification of the environment, policy, and training hyperparameters. Focus on applied reinforcement learning for continuous control, providing a concrete example implementation that can be studied or adapted for related tasks and experiments.
Use Cases
The repository helps researchers, students, and practitioners learn how PPO is implemented and applied to a continuous control problem using PyTorch. It serves as a hands-on example to study training dynamics, policy behavior, and to prototype algorithmic changes or alternative architectures. Engineers working on control or robotics tasks can adapt the custom environment and PPO code as a baseline for benchmarking or transfer to similar domains. The project supports education, experimentation, and reproducible-style development when exploring reinforcement learning solutions for landing and other physics-based control challenges.
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