Dynamic Programming and Reinforcement Learning (A.Y. 2023/24)

Contents

Dynamic Programming [RLOC - Ch. 1, 4], [S]

• Introduction to Markov Decision Processes: Dynamic Programming and Reinforcement Learning

• Review of probability calculus

• Tools for the simulation of stochastic systems

• Review of Markov chains

• Formulation of Markov Decision Problems (MDPs)

• Deterministic dynamic programming

• Stochastic dynamic programming

• Optimal stopping problems

• Dynamic programming over infinite time horizon

• Minimum time problems

Reinforcement Learning [RLI - Ch. 5, 6, 13]

• Approximate dynamic programming

• Q-Learning

• Value-function Approximation

• Policy Gradient Methods

Throughout the course, exercises will be worked out by using small Python scripts

Prerequisites

• Basic notions of probability and Markov Chains

• Prior knowledge of Python is useful, although not required