Reinforcement Learning

Spring 2024 - ECE 457C

ECE 457C - Reinforcement Learning

Offered Spring 2024 by Prof. Mark Crowley

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).

Course Outline

See the official course outline at : for course location, times, staff contact and other information .

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