Dr. Rina Dechter - University of California at Irvine ZOT!
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CompSci 295 Reinforcement Learning, Winter 2018

  • Classroom: DBH 1429
  • Day: Monday
  • Time: 4:00 - 6:00 pm
  • Instructor: Rina Dechter - dechter@ics.uci.edu

This seminar will focus on reinforcement learning.

Relevant sources:


  • Learning to Predict by the Methods of Temporal Differences [pdf]
    Richard S. Sutton
    Machine Learning, volume 3, pp 9-44, 1988.

  • Algorithms for Sequential Decision Making [pdf]
    Michael L. Littman
    Ph.D. Dissertation, Brown University, Providence, RI, USA, March 1996.

  • Reinforcement Learning: A Survey [pdf]
    Leslie Pack Kaelbling, Michael L. Littman and Andrew W. Moore
    Journal of Artificial Intelligence Research, volume 4, pp 237-285, 1996.

  • SPUDD: Stochastic Planning using Decision Diagrams [pdf]
    Jesse Hoey, Robert St-Aubin, Alan Hu and Craig Boutilier
    UAI-99. 15th Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, July 1999.

  • Near-Optimal Reinforcement Learning in Polynomial Time [pdf]
    Michael Kearns and Satinder Singh
    Machine Learning, volume 49, pp 209-232, 2002.

  • Equivalence notions and model minimization in Markov decision processes [pdf]
    Robert Givan, Thomas Dean and Matthew Greig
    Artificial Intelligence, volume 147, pp 163-223, 2003.

  • An Analytic Solution to Discrete Bayesian Reinforcement Learning [pdf]
    Pascal Poupart, Nikos Vlassis, Jesse Hoey, Kevin Regan
    ICML-06. 23rd International Conference on Machine Learning, Pittsburgh, PA, USA, June 2006.

  • Knows What It Knows: A Framework For Self-Aware Learning [pdf]
    Lihong Li, Michael L. Littman, Thomas J. Walsh
    ICML-08. 25th International Conference on Machine Learning, Helsinki, Finland, July 2008.

  • Monte-Carlo tree search and rapid action value estimation in computer Go [pdf]
    Sylvain Gelly and David Silver
    Artificial Intelligence, volume 175, pp 1856-1875, 2011.

  • A Survey of Monte Carlo Tree Search Methods [pdf]
    Cameron Browne, Edward Powley, Daniel Whitehouse, Simon Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis and Simon Colton
    IEEE Transactions on Computational Intelligence and AI in Games, volume 4, pp 1-43, 2012.

  • Policy evaluation using the Ω-return [pdf]
    Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Konidaris
    NIPS-15. 28th International Conference on Neural Information Processing Systems, Montreal, Canada, December 2015.

  • Mastering the game of Go without human knowledge [pdf]
    David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel and Demis Hassabis
    Nature, volume 550, pp 354–359, 2017.


  • DRLW-15. Deep Reinforcement Learning Workshop, NIPS 2015, Montreal, Canada, December 2015.

  • DRLW-16. Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, December 2016.

  • EWRL-16. The 13th European Workshop on Reinforcement Learning, Barcelona, Spain, December 2016.


Week           Date Topic Readings
Week 1 1/8

Week 2 1/15

Week 3 1/22

Week 4

Week 5 2/5

Week 6 2/12

Week 7 2/19

Week 8 2/26

Week 9

Week 10