Transfer and Multi-Task Learning in Reinforcement Learning Alessandro LAZARIC “Machine Learning with Interdependent and Non-identically Distributed Data”

Slides:

Advertisements

Similar presentations

© Jude Shavlik 2006, David Page 2007 CS 760 – Machine Learning (UW-Madison)RL Lecture, Slide 1 Reinforcement Learning (RL) Consider an “agent” embedded.

Advertisements

Tuning bandit algorithms in stochastic environments The 18th International Conference on Algorithmic Learning Theory October 3, 2007, Sendai International.

Extraction and Transfer of Knowledge in Reinforcement Learning A.LAZARIC Inria “30 minutes de Science” Seminars SequeL Inria Lille – Nord Europe December.

Ai in game programming it university of copenhagen Reinforcement Learning [Outro] Marco Loog.

Background Reinforcement Learning (RL) agents learn to do tasks by iteratively performing actions in the world and using resulting experiences to decide.

Towards Equilibrium Transfer in Markov Games 胡裕靖

1 Reinforcement Learning Introduction & Passive Learning Alan Fern * Based in part on slides by Daniel Weld.

COSC 878 Seminar on Large Scale Statistical Machine Learning 1.

CIS 678 Artificial Intelligence problems deduction, reasoning knowledge representation planning learning natural language processing motion and manipulation.

Reinforcement Learning

R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 1 Chapter 2: Evaluative Feedback pEvaluating actions vs. instructing by giving correct.

An Introduction to Machine Learning In the area of AI (earlier) machine learning took a back seat to Expert Systems Expert system development usually consists.

Mental Development and Representation Building through Motivated Learning Janusz A. Starzyk, Ohio University, USA, Pawel Raif, Silesian University of Technology,

1 Kunstmatige Intelligentie / RuG KI Reinforcement Learning Johan Everts.

Reinforcement Learning Presented by: Kyle Feuz.

Learning Programs Danielle and Joseph Bennett (and Lorelei) 4 December 2007.

Modelling Motivation for Experience-Based Attention Focus in Reinforcement Learning Candidate Kathryn Merrick School of Information Technologies University.

INTRODUCTION TO Machine Learning ETHEM ALPAYDIN © The MIT Press, Lecture Slides for.

Kunstmatige Intelligentie / RuG KI Reinforcement Learning Sander van Dijk.

Slide 1 Tutorial: Optimal Learning in the Laboratory Sciences Working with nonlinear belief models December 10, 2014 Warren B. Powell Kris Reyes Si Chen.

Training and future (test) data follow the same distribution, and are in same feature space.

Lyle Ungar, University of Pennsylvania Learning and Memory Reinforcement Learning.

Reinforcement Learning Evaluative Feedback and Bandit Problems Subramanian Ramamoorthy School of Informatics 20 January 2012.

Machine Learning1 Machine Learning: Summary Greg Grudic CSCI-4830.

Reinforcement Learning

General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning Duke University Machine Learning Group Discussion Leader: Kai Ni June 17, 2005.

Affective Computing: Agents With Emotion Victor C. Hung University of Central Florida – Orlando, FL EEL6938: Special Topics in Autonomous Agents March.

1 Dr. Itamar Arel College of Engineering Electrical Engineering & Computer Science Department The University of Tennessee Fall 2009 August 24, 2009 ECE-517:

Thesis Proposal PrActive Learning: Practical Active Learning, Generalizing Active Learning for Real-World Deployments.

Transfer Learning Task. Problem Identification Dataset : A Year: 2000 Features: 48 Training Model ‘M’ Testing 98.6% Training Model ‘M’ Testing 97% Dataset.

1 CSC 8520 Spring Paula Matuszek Kinds of Machine Learning Machine learning techniques can be grouped into several categories, in several ways: –What.

Reinforcement Learning

CPS 270: Artificial Intelligence Machine learning Instructor: Vincent Conitzer.

Reinforcement Learning 主講人：虞台文 Content Introduction Main Elements Markov Decision Process (MDP) Value Functions.

Relational Macros for Transfer in Reinforcement Learning Lisa Torrey, Jude Shavlik, Trevor Walker University of Wisconsin-Madison, USA Richard Maclin University.

Reinforcement Learning

Curiosity-Driven Exploration with Planning Trajectories Tyler Streeter PhD Student, Human Computer Interaction Iowa State University

Class 2 Please read chapter 2 for Tuesday’s class (Response due by 3pm on Monday) How was Piazza? Any Questions?

INTRODUCTION TO Machine Learning

Some questions -What is metadata? -Data about data.

CHAPTER 16: Reinforcement Learning. Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1) 2 Introduction Game-playing:

Design and Implementation of General Purpose Reinforcement Learning Agents Tyler Streeter November 17, 2005.

Course Overview  What is AI?  What are the Major Challenges?  What are the Main Techniques?  Where are we failing, and why?  Step back and look at.

Copyright Paula Matuszek Kinds of Machine Learning.

Reinforcement Learning AI – Week 22 Sub-symbolic AI Two: An Introduction to Reinforcement Learning Lee McCluskey, room 3/10

Transfer Learning in Sequential Decision Problems: A Hierarchical Bayesian Approach Aaron Wilson, Alan Fern, Prasad Tadepalli School of EECS Oregon State.

COMP 2208 Dr. Long Tran-Thanh University of Southampton Reinforcement Learning.

MIT Artificial Intelligence Laboratory — Research Directions Intelligent Agents that Learn Leslie Pack Kaelbling.

Web-Mining Agents: Transfer Learning TrAdaBoost R. Möller Institute of Information Systems University of Lübeck.

Introduction to Reinforcement Learning Hiren Adesara Prof: Dr. Gittens.

Reinforcement Learning Guest Lecturer: Chengxiang Zhai Machine Learning December 6, 2001.

Reinforcement Learning. Overview Supervised Learning: Immediate feedback (labels provided for every input). Unsupervised Learning: No feedback (no labels.

Reinforcement Learning Introduction Passive Reinforcement Learning Temporal Difference Learning Active Reinforcement Learning Applications Summary.

Network Management Lecture 13. MACHINE LEARNING TECHNIQUES 2 Dr. Atiq Ahmed Université de Balouchistan.

Transfer Learning and Intelligence: an Argument and Approach Matthew E. Taylor Joint work with: Gregory Kuhlmann and Peter Stone Learning Agents Research.

Brief Intro to Machine Learning CS539

Done Done Course Overview What is AI? What are the Major Challenges?

Reinforcement Learning

Transferring Instances for Model-Based Reinforcement Learning

Basic Intro Tutorial on Machine Learning and Data Mining

Tuning bandit algorithms in stochastic environments

Reinforcement Learning

Instructors: Fei Fang (This Lecture) and Dave Touretzky

Chapter 2: Evaluative Feedback

Lecture 6: Introduction to Machine Learning

Reinforcement Learning

MGT601 SME MANAGEMENT.

Deep Reinforcement Learning: Learning how to act using a deep neural network Psych 209, Winter 2019 February 12, 2019.

Chapter 2: Evaluative Feedback

Presentation transcript:

Transfer and Multi-Task Learning in Reinforcement Learning Alessandro LAZARIC “Machine Learning with Interdependent and Non-identically Distributed Data” SequeL Inria Lille – Nord Europe April 7-10, 2015

Reinforcement Learning April 7-10, 2015 A. LAZARIC – Transfer in RL- 2 agent environment critic delay <position, speed><handlebar, pedals><new position, new speed>, advancement Value Function Control Policy

Transfer in Reinforcement Learning April 7-10, 2015 A. LAZARIC – Transfer in RL- 3 agent environment critic delay transfer of knowledge

Transfer in RL is not trivial April 7-10, 2015 A. LAZARIC – Transfer in RL- 4 Techniques developed in supervised learning cannot be always re-used in RL: Many different “objects” that can be transferred (eg, policies, value functions, samples) Tasks may be similar in many different ways Samples are often non-iid “Unsupervised” samples are not well defined Different objectives (eg, exploration-exploitation)

My research (present and future): transfer for exploration-exploitation April 7-10, 2015 A. LAZARIC – Transfer in RL- 5 Motivating problems Intelligent tutoring systems Recommendation systems Computer games Attempted (successful) approaches in multi-armed bandit Identification of finite set of models Transfer of samples Open questions Estimation of the bias for selective transfer Appropriate measure of similarity Exploration vs exploitation vs transfer

Thanks!! Inria Lille – Nord Europe