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Author Information
Shalabh Bhatnagar (Indian Institute of Science)
Richard Sutton (DeepMind, U Alberta)
Richard S. Sutton is a professor and iCORE chair in the department of computing science at the University of Alberta. He is a fellow of the Association for the Advancement of Artificial Intelligence and co-author of the textbook "Reinforcement Learning: An Introduction" from MIT Press. Before joining the University of Alberta in 2003, he worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts. He received a PhD in computer science from the University of Massachusetts in 1984 and a BA in psychology from Stanford University in 1978. Rich's research interests center on the learning problems facing a decision-maker interacting with its environment, which he sees as central to artificial intelligence. He is also interested in animal learning psychology, in connectionist networks, and generally in systems that continually improve their representations and models of the world.
Mohammad Ghavamzadeh (Facebook AI Research)
Mark P Lee (University of Alberta)
Related Events (a corresponding poster, oral, or spotlight)
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2007 Spotlight: Incremental Natural Actor-Critic Algorithms »
Wed. Dec 5th 01:20 -- 01:30 AM Room
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2022 : On Convergence of Average-Reward Off-Policy Control Algorithms in Weakly-Communicating MDPs »
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2019 : Poster and Coffee Break 2 »
Karol Hausman · Kefan Dong · Ken Goldberg · Lihong Li · Lin Yang · Lingxiao Wang · Lior Shani · Liwei Wang · Loren Amdahl-Culleton · Lucas Cassano · Marc Dymetman · Marc Bellemare · Marcin Tomczak · Margarita Castro · Marius Kloft · Marius-Constantin Dinu · Markus Holzleitner · Martha White · Mengdi Wang · Michael Jordan · Mihailo Jovanovic · Ming Yu · Minshuo Chen · Moonkyung Ryu · Muhammad Zaheer · Naman Agarwal · Nan Jiang · Niao He · Nikolaus Yasui · Nikos Karampatziakis · Nino Vieillard · Ofir Nachum · Olivier Pietquin · Ozan Sener · Pan Xu · Parameswaran Kamalaruban · Paul Mineiro · Paul Rolland · Philip Amortila · Pierre-Luc Bacon · Prakash Panangaden · Qi Cai · Qiang Liu · Quanquan Gu · Raihan Seraj · Richard Sutton · Rick Valenzano · Robert Dadashi · Rodrigo Toro Icarte · Roshan Shariff · Roy Fox · Ruosong Wang · Saeed Ghadimi · Samuel Sokota · Sean Sinclair · Sepp Hochreiter · Sergey Levine · Sergio Valcarcel Macua · Sham Kakade · Shangtong Zhang · Sheila McIlraith · Shie Mannor · Shimon Whiteson · Shuai Li · Shuang Qiu · Wai Lok Li · Siddhartha Banerjee · Sitao Luan · Tamer Basar · Thinh Doan · Tianhe Yu · Tianyi Liu · Tom Zahavy · Toryn Klassen · Tuo Zhao · Vicenç Gómez · Vincent Liu · Volkan Cevher · Wesley Suttle · Xiao-Wen Chang · Xiaohan Wei · Xiaotong Liu · Xingguo Li · Xinyi Chen · Xingyou Song · Yao Liu · YiDing Jiang · Yihao Feng · Yilun Du · Yinlam Chow · Yinyu Ye · Yishay Mansour · · Yonathan Efroni · Yongxin Chen · Yuanhao Wang · Bo Dai · Chen-Yu Wei · Harsh Shrivastava · Hongyang Zhang · Qinqing Zheng · SIDDHARTHA SATPATHI · Xueqing Liu · Andreu Vall -
2019 : Panel Discussion »
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2019 : Panel Discussion led by Grace Lindsay »
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2019 : Invited Talk #7: Richard Sutton »
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2019 Workshop: Safety and Robustness in Decision-making »
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2019 Poster: Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies »
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2019 Spotlight: Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies »
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2018 Poster: A Lyapunov-based Approach to Safe Reinforcement Learning »
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2018 Poster: A Block Coordinate Ascent Algorithm for Mean-Variance Optimization »
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2017 Poster: Conservative Contextual Linear Bandits »
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2016 : Richard Sutton (University of Alberta) »
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2016 : Rich Sutton »
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2016 Poster: Safe Policy Improvement by Minimizing Robust Baseline Regret »
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2015 Workshop: Machine Learning for (e-)Commerce »
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2015 Poster: Policy Gradient for Coherent Risk Measures »
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2015 Tutorial: Introduction to Reinforcement Learning with Function Approximation »
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2014 Workshop: Representation and Learning Methods for Complex Outputs »
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2014 Poster: Algorithms for CVaR Optimization in MDPs »
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2014 Poster: Weighted importance sampling for off-policy learning with linear function approximation »
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2013 Poster: Actor-Critic Algorithms for Risk-Sensitive MDPs »
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2013 Poster: Approximate Dynamic Programming Finally Performs Well in the Game of Tetris »
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2013 Oral: Actor-Critic Algorithms for Risk-Sensitive MDPs »
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2012 Poster: Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence »
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2011 Poster: Multi-Bandit Best Arm Identification »
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2011 Invited Talk: Learning About Sensorimotor Data »
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2011 Poster: Speedy Q-Learning »
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2010 Spotlight: LSTD with Random Projections »
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2010 Poster: LSTD with Random Projections »
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2009 Poster: Multi-Step Dyna Planning for Policy Evaluation and Control »
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2009 Poster: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation »
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2009 Spotlight: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation »
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2008 Workshop: Model Uncertainty and Risk in Reinforcement Learning »
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2008 Poster: Regularized Policy Iteration »
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2008 Poster: A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approxi »
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2006 Poster: Bayesian Policy Gradient Algorithms »
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