Fund of Reinforcement Learning

Catalog description

This course will provide an introduction to the field of reinforcement learning (RL). The topics that will be covered (time permitting) include but not limited to Markov Chains; MDPs; Value Functions; Policy Iteration and Value Iteration; Monte Carlo Methods; Temporal Difference (TD) Learning; (Linear) Function Approximation; SARSA; Q-Learning; TD(); Actor-Critic Methods; Neural Networks, Backpropagation and Applications to RL; Other topics (e.g., Multi-Agent RL, RL Theory; Deep Reinforcement Learning). This course will emphasize on hands-on experiences, students are expected to become well versed in key ideas and techniques for RL through a combination of lectures, written and coding assignments. Students will advance their understanding and the field of RL through a project. Prerequisites: Calculus and Linear Algebra; Basic Probability and Statistics; Python.