Reinforcement Learning

This course provides a high-level overview of reinforcement learning at the intersection of machine learning and control theory. It covers model-based and model-free methods, dynamic programming, Q-learning, temporal-difference learning, and deep reinforcement learning for control.

Syllabus

Reinforcement Learning: Machine Learning Meets Control Theory.
Reinforcement Learning Series: Overview of Methods.
Model Based Reinforcement Learning: Policy Iteration, Value Iteration, and Dynamic Programming.
Q-Learning: Model Free Reinforcement Learning and Temporal Difference Learning.
Nonlinear Control: Hamilton Jacobi Bellman (HJB) and Dynamic Programming.
Deep Reinforcement Learning: Neural Networks for Learning Control Laws.
Overview of Deep Reinforcement Learning Methods.
Deep Reinforcement Learning for Fluid Dynamics and Control.