Multi-Step Reinforcement Learning: A Unifying Algorithm Kristopher De Asis, 1J. Fernando Hernandez-Garcia, G. Zacharias Holland, Richard S. Sutton Reinforcement Learning and Artificial Intelligence Laboratory, University of Alberta fkldeasis,jfhernan,gholland,rsuttong@ualberta.ca Abstract Unifying seemingly disparate algorithmic ideas to.

1. Reinforcement Learning Sutton Barto
2. Reinforcement Learning Richard Sutton Pdf

[results with direct download]

Reinforcement learning in the brain - Princeton University - Hom

Reinforcement learning in the brain Yael Niv Psychology Department & Princeton Neuroscience Institute, Princeton University Abstract: A wealth of research focuses on

Intrinsically Motivated Reinforcement Learning -

Intrinsically Motivated Reinforcement Learning Satinder Singh Computer Science & Eng. University of Michigan baveja@umich.edu Andrew G. Barto Dept. of Computer

Reinforcement learning in board games. -

Reinforcement learning in board games. Imran Ghory May 4, 2004 Abstract This project investigates the application of the TD( ) reinforcement learning algorithm and

Reinforcement Learning Sutton Barto

Reinforcement Learning for Elevator Control

Reinforcement Learning for Elevator Control? Xu Yuan? Lucian Bu¸soniu?? Robert Babu?ska?? ASML Netherlands B.V., The Netherlands (e-mail: xu.yuan@asml

THE COGNITIVE NEUROSCIENCE OF MOTIVATION

social cognition, vol. 26, no. 5, 2008, pp. 593–620 593 motivation and learning daw and shohamy the cognitive neuroscience of motivation and learning

Teaching Staff and Contact Info - Stanford University

Teaching Staff and Contact Info Professor: Andrew Ng Office: Gates 156 TA: Paul Baumstarck Office: B24B Tom Mitchell, Machine Learning. McGraw-Hill, 1997.

2002 Special issue Opponent interactions between

2002 Special issue Opponent interactions between serotonin and dopamine Nathaniel D. Dawa,*, Sham Kakadeb, Peter Dayanb aComputer Science Department

Evaluating Web-Based Learning Systems - AABRI

Journal of Instructional Pedagogies Evaluating web-based learning systems, Page 1 Evaluating Web-Based Learning

Dynamic Optimization for Optimal Control of

Dynamic Optimization for Optimal Control of Water Distribution Systems Emre Ertin, Anthony N. Dean, Mathew L. Moore and Kevin L. Priddy Battelle Memorial Institute

The Use of Reward Systems to Improve Behaviour

Dr A Merrett & Dr L Merrett May 2013 Page 1 of 6 The Use of Reward Systems to Improve Behaviour and Attainment in Schools Authors: Dr Anna Merrett & Dr Laura

Reinforcement Learning: An Introduction -

Book Next: Contents Contents Reinforcement Learning: An Introduction Richard S. Sutton and Andrew G. Barto A Bradford Book The MIT Press Cambridge, Massachusetts

Sutton, R.S. (1999). Reinforcement Learning.

Reinforcement Learning Richard S. Sutton January 28, 1999 Reinforcement learning is an approach to arti?cial intelligence that em-phasizes learning by the

Reinforcement Learning: An Introduction - Higher Intellect

Reinforcement Learning: An Introduction Richard S. Sutton and Andrew G. Barto MIT Press, Cambridge, MA, 1998 A Bradford Book Endorsements Code Solutions Figures

Sutton, R.S. (1992). Reinforcement Learning Architectures.

Reinforcement Learning Architectures Richard S. Sutton GTE Laboratories Incorporated Waltham, MA 02254 sutton@gte.com Abstract Reinforcement learning is the learning

Reinforcement Learning - Bryn Mawr Computer Science

R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction! 3! The Agent Learns a Policy! Reinforcement learning methods specify how the agent

Reinforcement Learning. Richard S. Sutton and Andrew G. Barto .

1.1 Reinforcement Learning note that the entire issue of balancing exploration and exploitation does not even arise in supervised learning as it is usually defined.

Generalization in Reinforcement Learning: Successful Examples .

Advances in Neural Information Processing Systems 8, pp. 1038-1044, MIT Press, 1996. Generalization in Reinforcement Learning: Successful Examples Using

Chapter 3: The Reinforcement Learning Problem

R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 1 Chapter 3: The Reinforcement Learning Problem describe the RL problem we will be studying for the

Policy Gradient Methods for Reinforcement Learning with

Advances in Neural Information Processing Systems 12, pp. 1057{1063, MIT Press, 2000 Policy Gradient Methods for Reinforcement Learning with Function

Chapter 1 Introduction

approach we explore, called reinforcement learning, is much more focused on Sutton, 1981a) led to our appreciation of the distinction between supervised.

Policy Gradient Methods for Reinforcement Learning with Function

Richard S. Sutton, David McAllester, Satinder Singh, Yishay Mansour. AT&T Labs Abstract. Function approximation is essential to reinforcement learning, but.

Introduction to Reinforcement Learning - UCL Computer Science

Lecture 1: Introduction to Reinforcement Learning. Admin. Textbooks. An Introduction to Reinforcement Learning, Sutton and. Barto, 1998. MIT Press, 1998 .

Recent Advances in Hierarchical Reinforcement Learning

Reinforcement learning is bedeviled by the curse of dimensionality: the number Reinforcement learning (RL) (Bertsekas and Tsitsiklis, 1996; Sutton and Barto

Generalization in Reinforcement Learning: Successful Examples .

On large problems, reinforcement learning systems must use parame- been proven stable in theory (Sutton, 1988; Dayan, 1992) and very effective in practice.

Reinforcement Learning: A Tutorial - Electrical Engineering and

Reinforcement Learning: A Tutorial. Satinder Singh. Computer Science & Engineering. University of Michigan, Ann Arbor with special thanks to. Rich Sutton?

Reinforcement Learning Richard Sutton Pdf

Python replication for Sutton & Barto's book Reinforcement Learning: An Introduction (2nd Edition)

If you have any confusion about the code or want to report a bug, please open an issue instead of emailing me directly. And unfortunately I do not have exercise answers for the book.

Chapter 1

1. Tic-Tac-Toe

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

• python 3.6
• numpy
• matplotlib

All files are self-contained

If you want to contribute some missing examples or fix some bugs, feel free to open an issue or make a pull request.

Following are missing figures/examples:

• Figure 12.14: The effect of λ