taherfattahi/ppo-rocket-landing
Proximal Policy Optimization (PPO) algorithm using PyTorch to train an agent for a rocket landing task in a custom environment
This project helps aerospace engineers and researchers develop and test AI agents for controlling rocket landings or hovering. It takes in simulated rocket physics data, including position, velocity, and angles, and outputs optimal thrust and nozzle adjustments. The end-user is typically someone working on spacecraft guidance, autonomous systems, or aerospace simulation.
243 stars. No commits in the last 6 months.
Use this if you need to train an autonomous agent to perform precise maneuvers for rocket landing or hovering in a simulated environment.
Not ideal if you are looking for a pre-built simulation for general rocket launch physics or orbital mechanics, rather than an agent training framework.
Stars
243
Forks
51
Language
Python
License
MIT
Category
Last pushed
Nov 02, 2024
Commits (30d)
0
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