Theory and Methods for Reinforcement Learning
EE-618
Lecture 2: Dynamic Programming.
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Description
MDPs; value and Q functions; value iteration, policy iteration; operator perspectives. Model-free policy-based and value-based methods; Monte Carlo (MC) method and temporal difference (TD) learning.