Reinforcement Learning

The primary focus of this course to learn how an intelligent agent behave in a simulated environment and how to design that environment. Application of the methods learned in this course in simulated environment, robotics, board game and other fields is also discussed. Major topics learned in this course includes: Agent-Environment Interface, Goals, Reward, Markov Decision Process (MDPs), Value functions, Optimality, Policy Evaluation and Improvement, Policy and Value Iteration, Monte Carlo Methods, Temporal Difference Learning (Q learning & SARSA) and varied versions of these methods.

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