Note: Also Known As ECE 493 Topic 42 - (Probabilistic Reasoning and) Reinforcement Learning

Course Description

Introduction to reinforcement learning (RL) theory and algorithms for learning decision-making policies in situations with uncertainty and limited information. Topics include Markov decision processes, classic exact/approximate RL algorithms such as value/policy iteration, Q-learning, State-action-reward-state-action (SARSA), Temporal Difference (TD) methods, policy gradients, actor-critic, and Deep RL such as Deep Q-Learning (DQN), Asynchronous Advantage Actor Critic (A3C), and Deep Deterministic Policy Gradient (DDPG). 

Required Background

The course will use concepts from ECE 203 and ECE 307 on Bayesian Probability and Statistics, these will be reviewed but familiarity will help significantly. All other concepts needed for the course will be introduced directly. Examples, assignments and projects will depend on programming ability in Python.