Reinforcement Learning

Coming soon — this chapter will cover the core ideas of reinforcement learning: agents, environments, rewards, policies, and value functions.

Overview

Reinforcement learning is how an agent learns to act in the world by trial and error, guided only by a reward signal. It sits at the intersection of control theory, optimization, and machine learning, and underpins much of modern robotics and game-playing AI.

What you’ll learn

• Markov decision processes and the Bellman equations
• Value-based methods: Q-learning, DQN
• Policy-based methods: REINFORCE, actor-critic
• How RL connects to control and robotics