Decision Making and Reinforcement Learning

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 n

• 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