comp 138 reinforcement learning
play

COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage : - PowerPoint PPT Presentation

Feb 07, 2023 •329 likes •717 views

COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage : https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL_Fall2020/ BE a reinforcement learner You, as a class, will act as the learning agent BE a reinforcement learner

  1. COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage : https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL_Fall2020/

  2. BE a reinforcement learner ● You, as a class, will act as the learning agent

  3. BE a reinforcement learner ● You, as a class, will act as the learning agent ● Actions: wave, clap, or nod

  4. BE a reinforcement learner ● You, as a class, will act as the learning agent ● Actions: wave, clap, or nod ● Observations: color, reward

  5. BE a reinforcement learner ● You, as a class, will act as the learning agent ● Actions: wave, clap, or nod ● Observations: color, reward ● Goal: find an optimal policy

  6. BE a reinforcement learner ● You, as a class, will act as the learning agent ● Actions: wave, clap, or stand ● Observations: color, reward ● Goal: find an optimal policy – What is a policy? What makes a policy optimal?

  7. How did you do it? ● What is your policy, and how is it represented? ● What does the world look like?

  8. What actually happened...

  14. What actually happened...

  15. Now, let’s formalize this (board or writing projector)

  16. About this course ● Reinforcement Learning theory & practice ● Theory at the start and practice towards end ● Syllabus = the course web page: https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL/

  17. Where does RL fall within the field of Artificial Intelligence?

  18. Where does RL fall within the field of Artificial Intelligence? ● AI → ML → RL

  19. Where does RL fall within the field of Artificial Intelligence? ● AI → ML → RL ● Type of Machine Learning: – Supervised : learn from labeled examples – Unsupervised : learn from unlabeled examples – Reinforcement : learn through interaction

  20. Reduced Formalism

  21. Reduced Formalism (board or writing projector)

  22. Take-home Message ● Agent’s perspective: only the policy is under control ● State representation and reward function are given ● Focus on policy algorithms ● Appeal: program agents by just specifying goals ● Practice: need to pick state representation and reward function

  23. Example Applications

  24. Example Applications

  25. Reading Assignment ● Chapter 1 and 2 of Sutton and Barto ● Reading response on Canvas due 9/11 before class starts

  26. Programming Assignments ● Students are required to complete 4 minor programming assignments of their choosing ● Default options: programing exercises from Sutton and Barto (let’s look at some examples)

  27. Discussion Moderation ● Each student will lead a reading discussion once during the semester ● Students can team up in a pair ● Sign up sheet will be posted to Canvas tonight ● Extra credit for anyone who volunteers for slots in the next week ● Presentation materials / notes or description of what will be discussed should be emailed to me 48 hours before the class

  28. Next time...

  31. COMP 150: Reinforcement Learning

  32. Domains and Applications

  33. Curriculum Learning . . . . . . Example QuickChess game variants

  34. The Curriculum Learning Problem Task = MDP Environment Task Creatjon State Actjon Reward Agent Target task Sequencing Transfer Learning [ Narverkar et al 2016 ]

  35. Textbook The authors have made the book available: http://incompleteideas.net/book/bookdraft2017nov5.pdf

  36. Course Organization ● Taught as a seminar: students take turns presenting the readings ● Will cover both theory and practice ● Final projects – you will complete a project in which you ask (and then answer) a relevant RL research question

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage :

COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage :

COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage : https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL_Fall2020/ Announcements Homework 1 is due this Friday Need Help? Let Andre or I know Reading Assignment

645 views • 28 slides
COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage :

COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage :

COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage : https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL_Fall2020/ Announcements Reading Assignment Chapter 6 of Sutton and Barto Research Article Topics Transfer

480 views • 33 slides
RL Overview of topics About Reinforcement Learning The Reinforcement Learning Problem

RL Overview of topics About Reinforcement Learning The Reinforcement Learning Problem

Introduction to Reinforcement Learning RL Overview of topics About Reinforcement Learning The Reinforcement Learning Problem Inside an RL agent Temporal difference learning Many faces of Reinforcement Learning What is

551 views • 35 slides
Reinforcement Learning Reinforcement Learning Reinforcement Learning in a nutshell g Imagine

Reinforcement Learning Reinforcement Learning Reinforcement Learning in a nutshell g Imagine

Reinforcement Learning Reinforcement Learning Reinforcement Learning in a nutshell g Imagine playing a new game whose rules you dont know; after a hundred or so moves your don t know; after a hundred or so moves, your opponent announces, You

511 views • 30 slides
Reinforcement Learning You can think of supervised learning as the teacher providing answers

Reinforcement Learning You can think of supervised learning as the teacher providing answers

Reinforcement Learning You can think of supervised learning as the teacher providing answers (the class labels) Reinforcement learning: In reinforcement learning, the agent learns Markov Decision Processes based on a punishment/reward

89 views • 6 slides
Examples of Reinforcement Learning Robocup Soccer Teams Stone & Veloso, Reidmiller et al.

Examples of Reinforcement Learning Robocup Soccer Teams Stone & Veloso, Reidmiller et al.

Reinforcement Learning Slides by Rich Sutton Mods by Dan Lizotte Refer to Reinforcement Learning: An Introduction by Sutton and Barto Alpaydin Chapter 16 Up until now we have been Supervised Learning Classifying, mostly

891 views • 31 slides
CS885 Reinforcement Learning Module 2: June 6, 2020 Maximum Entropy Reinforcement Learning

CS885 Reinforcement Learning Module 2: June 6, 2020 Maximum Entropy Reinforcement Learning

CS885 Reinforcement Learning Module 2: June 6, 2020 Maximum Entropy Reinforcement Learning Haarnoja, Tang et al. (2017) Reinforcement Learning with Deep Energy Based Policies, ICML . Haarnoja, Zhou et al. (2018) Soft Actor-Critic: Off-Policy

682 views • 24 slides
Reinforcement Learning Reinforcement Learning Now that you know a little about Optimal Control

Reinforcement Learning Reinforcement Learning Now that you know a little about Optimal Control

Reinforcement Learning Reinforcement Learning Now that you know a little about Optimal Control Theory, you actually have some knowledge in RL. RL shares the overall goal with OCT: solving for a control policy such that the cumulative cost

610 views • 22 slides
Reinforcement Learning CS 4100: Artificial Intelligence Reinforcement Learning II Still

Reinforcement Learning CS 4100: Artificial Intelligence Reinforcement Learning II Still

Reinforcement Learning CS 4100: Artificial Intelligence Reinforcement Learning II Still assume a Marko kov decision process (MDP): A se set o of st states s s S A se set o of a actions ( s (per st state) A A mo model

96 views • 5 slides
R i f R i f Reinforcement Learning III Reinforcement Learning III t L t L i i III III Dec

R i f R i f Reinforcement Learning III Reinforcement Learning III t L t L i i III III Dec

R i f R i f Reinforcement Learning III Reinforcement Learning III t L t L i i III III Dec 03 2008 1 Large State Spaces h When a problem has a large state space we can not longer represent the U or Q functions as explicit tables explicit

436 views • 18 slides
Reinforcement Learning UMaine COS 470/570 Introduction to AI Why reinforcement learning?

Reinforcement Learning UMaine COS 470/570 Introduction to AI Why reinforcement learning?

Reinforcement Learning<br/><br/> 4/25/19, 8*06 PM Reinforcement Learning<br/><br/> 4/25/19, 8*06 PM Reinforcement Learning UMaine COS 470/570 Introduction to AI Why reinforcement learning? Spring 2019 Created:

371 views • 15 slides
7. Motor Control and Reinforcement Learning Outline A. Action Selection and Reinforcement B.

7. Motor Control and Reinforcement Learning Outline A. Action Selection and Reinforcement B.

7. Motor Control and Reinforcement Learning Outline A. Action Selection and Reinforcement B. Temporal Difference Reinforcement Learning C. PVLV Model D. Cerebellum and Error-driven Learning 2/23/18 COSC 494/594 CCN 2 Sensory-Motor Loop

790 views • 56 slides
Deep Reinforcement Learning [Mastering the Game of Go with Deep Reinforcement Learning and Tree

Deep Reinforcement Learning [Mastering the Game of Go with Deep Reinforcement Learning and Tree

Deep Reinforcement Learning [Mastering the Game of Go with Deep Reinforcement Learning and Tree Search, Nature 2016] CS 486/686 University of Waterloo Lecture 21: July 12, 2017 Outline AlphaGo Supervised Learning of Policy Networks

540 views • 15 slides
Reinforcement Learning CS 4100: Artificial Intelligence Reinforcement Learning Ja Jan-Wi

Reinforcement Learning CS 4100: Artificial Intelligence Reinforcement Learning Ja Jan-Wi

Reinforcement Learning CS 4100: Artificial Intelligence Reinforcement Learning Ja Jan-Wi Willem van de Meent Northeastern University [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188

145 views • 10 slides
Reinforcement Learning AIMA Chapters: 21.1, 21.2, 21.3. Sutton and Barto, Reinforcement Learning:

Reinforcement Learning AIMA Chapters: 21.1, 21.2, 21.3. Sutton and Barto, Reinforcement Learning:

Reinforcement Learning Reinforcement Learning AIMA Chapters: 21.1, 21.2, 21.3. Sutton and Barto, Reinforcement Learning: an Introduction, 2nd Edition: Chapters 6 (6.1 6.5) Outline Reinforcement Learning Reinforcement Learning: the

587 views • 27 slides
Module 11 Introduction to Reinforcement Learning CS 886 Sequential Decision Making and

Module 11 Introduction to Reinforcement Learning CS 886 Sequential Decision Making and

Module 11 Introduction to Reinforcement Learning CS 886 Sequential Decision Making and Reinforcement Learning University of Waterloo Machine Learning Supervised Learning Teacher tells learner what to remember Reinforcement Learning

401 views • 29 slides
Digital Hardware Design Why is it still so hard? Philipp Wagner The story of Ton Lear FOSDEM

Digital Hardware Design Why is it still so hard? Philipp Wagner The story of Ton Lear FOSDEM

Digital Hardware Design Why is it still so hard? Philipp Wagner The story of Ton Lear FOSDEM 2016 Digital Hardware Design Why is it still so hard? The story of Not Real Ton Lear FOSDEM 2016 Digital Hardware Design Why is it still

698 views • 47 slides
Graded Work Schedule Client Side Web Application Development George Corser, PhD Graded work

Graded Work Schedule Client Side Web Application Development George Corser, PhD Graded work

Graded Work Schedule Client Side Web Application Development George Corser, PhD Graded work Points Grading component 0 6 Assignments (not graded, but they are part of the final presentation) 0 Exercises (not graded, designed only to help

451 views • 9 slides
Academic Senate Meeting 11-03-20 El Camino College Quick Reminder Comments/questions not

Academic Senate Meeting 11-03-20 El Camino College Quick Reminder Comments/questions not

Academic Senate Meeting 11-03-20 El Camino College Quick Reminder Comments/questions not directly related to current agenda item will not be acknowledged until public comment. Additionally, comments/questions on agenda items should

504 views • 38 slides
Governance Body In-Person Meeting Thursday, January 19, 2017 Task Force for Global Health Phone:

Governance Body In-Person Meeting Thursday, January 19, 2017 Task Force for Global Health Phone:

Governance Body In-Person Meeting Thursday, January 19, 2017 Task Force for Global Health Phone: 1-866-516-9291 Part III: Workshop for the Future Seat of your choice Governance Body Reps and ex officios Reconvene, agenda, and reflections

470 views • 25 slides
Without Losing Performance A big.LITTLE Software Strategy Klaas van Gend FAE, Trainer &

Without Losing Performance A big.LITTLE Software Strategy Klaas van Gend FAE, Trainer &

Cut Power Consumption by 5x Without Losing Performance A big.LITTLE Software Strategy Klaas van Gend FAE, Trainer & Consultant The mandatory Klaas-in-a-Plane picture LINUXCON EUROPE 2014 2 | October 10, 2014 Quad Core vs. Dual Core

524 views • 38 slides
Learning Management System (LMS) Evolution Change management perspective William Wan

Learning Management System (LMS) Evolution Change management perspective William Wan

Learning Management System (LMS) Evolution Change management perspective William Wan (william.wan@ust.hk) Head of Teaching Technologies Information Technology Services Center, HKUST Background of HKUST Background of HKUST Started in 1991

489 views • 31 slides
Annual meeting of shareholders May 13, 2020 1 Forward-looking statements From time to time

Annual meeting of shareholders May 13, 2020 1 Forward-looking statements From time to time

Annual meeting of shareholders May 13, 2020 1 Forward-looking statements From time to time Home Capital Group Inc. (the Company) makes written and verbal forward-looking statements. These are included in the Annual Report, periodic reports to

558 views • 19 slides
FBR Capital Markets & Co.: Boston Roadshow December 12-13, 2013 Cautionary Language This

FBR Capital Markets & Co.: Boston Roadshow December 12-13, 2013 Cautionary Language This

FBR Capital Markets & Co.: Boston Roadshow December 12-13, 2013 Cautionary Language This presentation contains statements, estimates and projections which are forward-looking statements (as defined in Section 21E of the Securities Exchange

830 views • 44 slides

More recommend