1 / 15

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning. Hiren Adesara Prof: Dr. Gittens. Sources for this presentation. Lecture videos of Mr. Satinder Singh, University of Michigan. Douglas Aberdeen, Australian National University. From www.videolectures.net

neola
Download Presentation

Introduction to Reinforcement Learning

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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)

  3. 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.

  4. Demo..

  5. 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.

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

  7. Markov Decision Process

  8. N

  9. Temporal Differences

  10. Questions ????

More Related