This repository contains a project that leverages reinforcement learning to make a humanoid robot walk in a PyBullet simulation. It uses a custom Gym environment, a Proximal Policy Optimization (PPO) agent, and a provided URDF file for the robot model. The training process prints rewards per generation and visualizes the robot's behavior.
☆11Jun 24, 2024Updated 2 years ago
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