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What you'll learn

Map between qualitative preferences and appropriate quantitative utilities.

Model non-associative and associative sequential decision problems with multi-armed bandit problems and Markov decision processes respectively

Implement dynamic programming algorithms to find optimal policies

Implement basic reinforcement learning algorithms using Monte Carlo and temporal difference methods

Skills you'll gain

Probability & Statistics

Machine Learning

Simulations

Reinforcement Learning

Artificial Intelligence and Machine Learning (AI/ML)

Data-Driven Decision-Making

Markov Model

Decision Support Systems

Algorithms

There are 8 modules in this course

This course is an introduction to sequential decision making and reinforcement learning. We start with a discussion of utility theory to learn how preferences can be represented and modeled for decision making. We first model simple decision problems as multi-armed bandit problems in and discuss several approaches to evaluate feedback. We will then model decision problems as finite Markov decision processes (MDPs), and discuss their solutions via dynamic programming algorithms. We touch on the notion of partial observability in real problems, modeled by POMDPs and then solved by online planning methods. Finally, we introduce the reinforcement learning problem and discuss two paradigms: Monte Carlo methods and temporal difference learning. We conclude the course by noting how the two paradigms lie on a spectrum of n-step temporal difference methods. An emphasis on algorithms and examples will be a key part of this course.

Bandit Problems

Markov Decision Processes

Dynamic Programming

Partially Observable Markov Decision Processes

Monte Carlo Methods

Temporal-Difference Learning

Reinforcement Learning - Generalization