all.cs.umass.eduAutonomous Learning Laboratory

Toggle navigation Home -- People Publications Join AI Safety Internal College of Information and Computer Sciences University of Massachusetts Amherst -- The (ALL) conducts foundational artificial intelligence (AI) research, with emphases on AI safety and reinforcement learning (RL), and particularly the intersection of these two areas. The long-term goals of the laboratory are to develop more capable artificial agents, ensure that systems that use artificial intelligence methods are safe and well-behaved, improve our understanding of biological learning and its neural basis, and to forge stronger links between studies of learning by computer scientists, engineers, neuroscientists, and psychologists. People Director Philip S. Thomas Director pthomas@cs.umass.edu Staff Sarah Byrne Grants and Contracts Coordinator sbyrne@cs.umass.edu Gwyn Mitchell Grants and Contracts Coordinator mitchell@cs.umass.edu -- Doctoral Students Stephen Giguere PhD Candidate sgiguere@cs.umass.edu Chris Nota PhD Student cnota@cs.umass.edu James Kostas PhD Student jkostas@cs.umass.edu Yash Chandak PhD Student ychandak@cs.umass.edu Blossom Metevier PhD Student bmetevier@cs.umass.edu Scott Jordan PhD Student sjordan@cs.umass.edu Mengxue Zhang PhD Student mengxuezhang@cs.umass.edu Alumni Directors Sridhar Mahadevan Director Not accepting new students mahadeva@cs.umass.edu Andrew Barto Founder Not accepting new students barto@cs.umass.edu Doctoral Students Name Adviser Year Current Website Francisco Garcia Philip S. Thomas 2019 link Clemens Rosenbaum Sridhar Mahadevan 2019 link Ian Gemp Sridhar Mahadevan 2019 link Thomas Boucher Sridhar Mahadevan 2018 link CJ Carey Sridhar Mahadevan 2017 link Bo Liu Sridhar Mahadevan 2015 link Chris Vigorito Andrew Barto 2015 link Philip Thomas Andrew Barto 2015 link Bruno Castro da Silva Andrew Barto 2015 link William Dabney Andrew Barto 2014 link Scott Niekum Andrew Barto 2013 link Yariv Z. Levy Andrew Barto 2012 link Scott Kuindersma Andrew Barto 2012 link George Konidaris Andrew Barto 2011 link Jeffrey Johns Sridhar Mahadevan 2010 link Chang Wang Sridhar Mahadevan 2010 link Alicia "Pippin" Peregrin Wolfe Andrew Barto 2010 link Sarah Osentoski Sridhar Mahadevan 2009 link Ashvin Shah Andrew Barto 2008 link Özgür Şimşek Andrew Barto 2008 link Khashayar Rohanimanesh Sridhar Mahadevan 2006 Mohammad Ghavamzadeh Sridhar Mahadevan 2005 link Anders Jonsson Andrew Barto 2005 link Thomas Kalt Andrew Barto 2005 Balaraman Ravindran Andrew Barto 2004 link Michael Rosenstein Andrew Barto 2003 Michael Duff Andrew Barto 2002 Amy McGovern Andrew Barto 2002 link Theodore Perkins Andrew Barto 2002 link Doina Precup Andrew Barto 2000 link Bob Crites Andrew Barto 1996 S. J. Bradtke Andrew Barto 1994 Satinder Singh Andrew Barto 1993 link J. R. Backrach Andrew Barto 1992 link Vijaykumar Gullapalli Andrew Barto 1992 Robert A. Jacobs Andrew Barto 1990 link J. S. Judd Andrew Barto 1988 Charles W. Anderson Andrew Barto 1986 link Richard S. Sutton Andrew Barto 1984 link Postdocs Name Adviser Year Current Website Jay Buckingham Andrew Barto Michael Kositsky Andrew Barto 1998 - 2001 Matthew Schlesinger Andrew Barto 1998 - 2000 link Andrew H. Fagg Andrew Barto 1998 - 2004 link Sascha E. Engelbrecht Andrew Barto 1996 - 2002 Vijaykumar Gullapalli Andrew Barto 1992 - 1994 Michael Jordan Andrew Barto link Masters and Bachelors Students Name Adviser Year Degree Sarah Brockman P. S. Thomas 2019 BS Michael Amirault P. S. Thomas 2018 BS Stefan Dernbach Sridhar Mahadevan 2015 MS Jonathan Leahey Sridhar Mahadevan 2013 MS Jie Chen Sridhar Mahadevan 2013 MS Andrew Stout Andrew Barto 2011 MS Armita Kaboli Andrew Barto 2011 MS Peter Krafft Andrew Barto 2010 BS Colin Barringer Andrew Barto 2007 MS Suchi Saria Sridhar Mahadevan 2002 - 2004 BS Eric Sondhi Sridhar Mahadevan BS Ilya Scheidwasser Sridhar Mahadevan BS Publications 2020 C. Nota and P. S. Thomas Is the Policy Gradient a Gradient? In AAMAS 2020 . Y. Chandak, G. Theocharous, C. Nota, and P. S. Thomas Lifelong Learning with a Changing Action Set In AAAI 2020 . [ arXiv ] Y. Chandak, G. Theocharous, B. Metevier, P. S. Thomas Reinforcement Learning When All Actions are Not Always Available In AAAI 2020 . [ arXiv ] 2019 P. S. Thomas, B. Castro da Silva, A. G. Barto, S. Giguere, Y. Brun, and E. Brunskill. Preventing Undesirable Behavior of Intelligent Machines In Science vol. 366, Issue 6468, pages 999–1004, 2019. [ link ] [ supplementary materials ] F. Garcia and P. S. Thomas A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning In NeurIPS 2019 [ link ] [ pdf ] Y. Chandak, G. Theocharous, J. Kostas, S. Jordan, and P. S. Thomas Learning Action Representations for Reinforcement Learning In ICML , 2019. [ arXiv ] [ pdf ] P. S. Thomas and E. Learned-Miller Concentration Inequalities for Conditional Value at Risk In ICML, 2019. [ pdf ] S. Tiwari and P. S. Thomas Natural Option Critic In AAAI , 2019. [ arXiv ] [ pdf ] Y. Chandak, G. Theocharous, J. Kostas, S. Jordan, and P. S. Thomas Improving Generalization over Large Action Sets At RLDM, 2019. C. Nota and P. S. Thomas Is the Policy Gradient a Gradient? [ arXiv ] P. S. Thomas, S. Jordan, Y. Chandak, C. Nota, and J. Kostas Classical Policy Gradient: Preserving Bellman's Principle of Optimality [ arXiv ] E. Learned-Miller and P.S. Thomas A New Confidence Interval for the Mean of a Bounded Random Variable [ arXiv ] [ pdf ] J. Kostas, C. Nota, and P. S. Thomas Asynchronous Coagent Networks: Stochastic Networks for Reinforcement Learning without Backpropagation or a Clock [ arXiv ] [ pdf ] 2018 P. S. Thomas, C. Dann, and E. Brunskill. Decoupling Gradient-Like Learning Rules from Representations In ICML , 2018. [ pdf ] C. Rosenbaum, T. Klinger, and M. Riemer Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning In ICLR , 2018. [ pdf ] M. Machado, C. Rosenbaum, X. Guo, M. Liu, G. Tesauro, and M. Campbell Eigenoption Discovery through the Deep Successor Representation In ICLR , 2018. [ pdf ] Y. Chandak, G. Theocharous, J. Kostas, and P. S. Thomas Reinforcement Learning with a Dynamic Action Set In Continual Learning workshop , NIPS 2018. S. M. Jordan, D. Cohen, and P. S. Thomas Using Cumulative Distribution Based Performance Analysis to Benchmark Models In Critiquing and Correcting Trends in ML workshop , NIPS 2018. [ pdf ] S. Giguere and P. S. Thomas. Classification with Probabilistic Fairness Guarantees Presented at FairWare , 2018. A. Jagannatha, P. S. Thomas, and H. Yu. Towards High Confidence Off-Policy Reinforcement Learning for Clinical Applications Presented at CausalML , 2018. [ pdf ] 2017 I. Durugkar, I. Gemp, and S. Mahadevan Generative Multi-Adversarial Networks In ICLR , 2017. [ pdf ] X. Guo, T. Klinger, C. Rosenbaum, J. P. Bigus, M. Campbell, B. Kawas, K. Talamadupula, G. Tesauro, and S. Singh Learning to Query, Reason, and Answer Questions On Ambiguous Texts In ICLR , 2017. [ pdf ] C. Rosenbaum, T. Gao, and T. Klinger e-QRAQ: A Multi-turn Reasoning Dataset and Simulator with Explanations In WHI@ICML , 2017. [ pdf ] 1978 – 2016 Click here for a listing of older publications. Joining Prospective Doctoral Students: The is not accepting applications for doctoral students at this time. Prospective Interns: The is not accepting applications for interns at any level at this time. Prospective Masters Students: The is not accepting applications for masters level positions at this time. Prospective Postdoctoral Researchers: The is not accepting applications for postdoctoral researchers at this time....