CS7642 Machine Learning
The course explores automated decision-making from a computational perspective through a combination of classic papers and more recent work. It examines efficient algorithms, where they exist, for learning single-agent and multi-agent behavioral policies and approaches to learning near-optimal decisions from experience.
Topics include Markov decision processes, stochastic and repeated games, partially observable Markov decision processes, reinforcement learning, deep reinforcement learning, and multi-agent deep reinforcement learning. Of particular interest will be issues of generalization, exploration, and representation. We will cover these topics through lecture videos, paper readings, and the book Reinforcement Learning by Sutton and Barto.
Students will replicate a result in a published paper in the area and work on more complex environments, such as those found in the OpenAI Gym library. Additionally, students will train agents to solve a more complex, multi-agent environment, namely the Google Research Football environment, and will have an opportunity to develop state-of-the-art or novel techniques.
The ability to run Docker locally or utilize a cloud computing service is strongly recommended. The instructional staff will not provide technical support or cloud computing credits
Lectures
- Week 1 - Smoov & Curly’s Bogus Journey
- Week 1 - Supplementary Introduction to Reinforcement Learning
- Week 1 - Chapter 3, Finite Markov Decision Process Links
- Central
- OMSCS page
- Course textbook
- Week 2 - Reinforcement learning basics
- Week 2 - Temporal Difference learning
- Week 3 - Supplementary Planning methods