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Lecture21
This document provides an introduction to reinforcement learning. It discusses how reinforcement learning aims to learn behaviors through trial-and-error interaction with an environment to maximize rewards. The document outlines the basic components of a reinforcement learning problem including states, actions, rewards, and policies. It provides examples of reinforcement learning problems like pole balancing and the mountain car problem to illustrate these concepts. The next class will cover how to learn policies to solve reinforcement learning problems.
