Train a Humanoid Robot with AI and Python

Training a humanoid robot can be achieved using Artificial Intelligence, specifically Reinforcement Learning (RL) within a 3D simulation. This approach uses Python with libraries like Gym and MuJoCo to create an environment where an agent learns through trial and error. The agent's goal is to maximize a cumulative reward, which is determined by a reward function that encourages desired behaviors like standing and moving forward. The tutorial explains core concepts like action spaces, observation spaces, and episodes, progressing from random actions to a basic RL algorithm before introducing Deep Reinforcement Learning for more complex challenges. This sim-to-real method allows for cheaper and faster training compared to using physical hardware.