# Intro to Reinforcement Learning

A brief introduction the reinforcement learning problem, including Markov decision processes (MDPs), policy search, and value estimation. Covered in the intro lecture. Lecture notes

#### Topics

• Sequential decision making under uncertainty

• Markov Decision Processes (MDP)

• Challenges: credit assignment, exploration

• Policy search:

• Ways to represent policies

• Ways to search for policies

• Value estimation:

• Value function and Q function

• Bellman equation and Bellman optimality equation

• Dynamic programming and value iteration

• TD learning and Q-learning

• Policy iteration
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