Introduction to Reinforcement Learning

1. Introduction to Reinforcement Learning HirenAdesara Prof: Dr. Gittens

2. Sources for this presentation • Lecture videos of • Mr. Satinder Singh, University of Michigan. • Douglas Aberdeen, Australian National University. From www.videolectures.net • Book : Introduction to Reinforcement Learning by Sutton and Barto (http://www.cs.ualberta.ca/%7Esutton/book/ebook/the-book.html)

4. Another View of RL • Observation-Action-Response. • O1a1r1o2a2r2o3a3r3 • Agent chooses action so as to maximize expected cumulative reward over time. • Observations can be vectors or other structures. • Actions are multi-dimensional. • Rewards are scalar. (known or unknown). • Agents have partial knowledge about environment.

5. Demo..

7. RL and Machine Learning • Supervised Learning • Learning approach to regression and classification. • Learning from example and learning from teacher. • Unsupervised learning • Learning approaches to dimensionality reduction, density estimation and recording data based on some principles. • Reinforcement Learning • Learning approaches to sequential decision making. • Learning from critics, learning from delayed reward.

8. Key ideas of RL • Markov Decision Process(MDP). • Temporal Differences( updating a guess on the basis of the previous guess). • Functional approximation.

9. Markov Decision Process

10. N

11. Temporal Differences

15. Questions ????