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Chapter 5: Monte Carlo Methods - UMass Amherst

R. S. Sutton and A. G. Barto: reinforcement learning : An Introduction1 Chapter 5: Monte Carlo Methods ! Monte Carlo Methods learn from complete sample returns!Only defined for episodic tasks! Monte Carlo Methods learn directly from experience!On-line: No model necessary and still attains optimality!Simulated: No need for a full modelR. S. Sutton and A. G. Barto: reinforcement learning : An Introduction2 Monte Carlo Policy Evaluation!Goal: learn V!(s)!Given: some number of episodes under ! which contain s!Idea: Average returns observed after visits to s!Every-Visit MC: average returns for every time s is visitedin an episode!

R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 1 Chapter 5: Monte Carlo Methods!Monte Carlo methods learn from complete sample returns! Only deÞned for episodic tasks ... Reinforcement Learning: An Introduction 9 Monte Carlo Estimation of Action Values (Q)!Monte Carlo is most useful when a model is not available!

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