Reinforcement Learning Basics

Agents learn by interacting with an environment. Rewards shape behavior through the Markov Decision Process framework — the basis of game-playing AI.

Learning Objectives

• →Define states, actions, rewards, and policy
• →Compute expected return with discounting
• →Implement a simple epsilon-greedy policy
• →Understand the Q-value Bellman equation

Practice