Reinforcement Learning
Spring 2025 - ECE 457C
ECE 457C - Reinforcement Learning
Offered Spring 2025 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 : https://outline.uwaterloo.ca/view/nmns2a for course location, times, staff contact and other information .
Additional Resource Links
This page will have additional resources linking to previous courses, topic notes etc, which may also be duplicated on LEARN.
- Schedule from a previous year as a guide: Full Weekly Schedule and Deadlines
- Piazza Discussions: TBD
- Textbook (online, free)
- Course YouTube Channel (old lecture videos from previous years, not updated recently))
Course News and Announcements
These will generally be posted into LEARN so that everyone can get a notification of announcements and updates. Be sure to enable notifications for course announcements.
See the official course outline at : https://outline.uwaterloo.ca/view/nmns2a for course location, times, staff contact and other information .