taherfattahi/ppo-rocket-landing
Proximal Policy Optimization (PPO) algorithm using PyTorch to train an agent for a rocket landing task in a custom environment
Implements a physics-based rocket environment with 8-dimensional state vectors (position, velocity, angle, nozzle orientation) and 9 discrete actions combining three thrust levels with nozzle angular velocities, enabling the agent to learn hover and landing control. Features separate actor-critic networks with configurable learning rates, PPO clipping parameters, and checkpoint/logging infrastructure. Built on PyTorch with optional GPU acceleration via CUDA, plus real-time training visualization and a dedicated evaluation script.
243 stars. No commits in the last 6 months.
Stars
243
Forks
51
Language
Python
License
MIT
Category
Last pushed
Nov 02, 2024
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