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Reinforcement Learning

Syllabus

A. Mathematical foundations

1. Stochastic Gradient Descent

2. Markov chains

3. Markov Decision processes

4. Dynamic programming

5. Bellman's equations

B. Monte Carlo Method and Machine Learning

1. Monte Carlo prediction

2. Monte Carlo Control
3. Monte Carlo methods and Markov chains: application to the location of robots.

Reinforcement Learning

A. Introduction: reward based problems

B. Return, Policy and Value Functions

C. Tabular Methods
D. Applications

E. Deep and Reinforcement Learning