Robotics with Python: Q-Learning vs Actor-Critic vs Evolutionary Algorithms

Reinforcement Learning (RL) is introduced as a method for training an AI agent to take actions in an environment to maximize a cumulative reward. The article demonstrates how to set up a standard 3D robotics environment, the "Ant" agent, using Python's Gymnasium library and the MuJoCo physics engine. The primary focus is on creating a custom environment by modifying the agent's physical properties in its XML definition file to make it lighter and stronger. It then shows how to subclass the environment's Python class to implement a new reward function that incentivizes the agent for gaining vertical height. This customized setup successfully retrains the agent to perform a jumping behavior instead of its original goal of forward locomotion.