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Suppose an agent is in a (possibly unknown) Markov Decision Process in the absence of a reward signal, what might we hope that an agent can efficiently learn to do? This work studies a broad class of objectives that are defined solely as functions of the state-visitation frequencies that are induced by how the agent behaves.
An algorithm for quadratic programming
Marguerite Frank and Philip Wolfe · 1956
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Learning to achieve goals
Leslie Pack Kaelbling · 1993
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y. Ng, Daishi Harada, and Stuart J. Russell · 1999
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour · 2000
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Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
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The linear programming approach to approximate dynamic programming
Daniela Pucci De Farias and Benjamin Van Roy · 2003
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On the sample complexity of reinforcement learning
Sham Machandranath Kakade · 2003
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y. Ng · 2004
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Dynamic programming and optimal control
Dimitri P Bertsekas · 2005
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Intrinsically motivated reinforcement learning
Nuttapong Chentanez, Andrew G. Barto, and Satinder P. Singh · 2005
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Pac model-free reinforcement learning
Alexander L Strehl, Lihong Li, Eric Wiewiora, John Langford, and Michael L Littman · 2006
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Where do rewards come from?
Satinder Singh, Richard L. Lewis, and Andrew G. Barto · 2009
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Intrinsically motivated reinforcement learning: An evolutionary perspective
Satinder P. Singh, Richard L. Lewis, Andrew G. Barto, and Jonathan Sorg · 2010
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Model-based reinforcement learning with nearly tight exploration complexity bounds
István Szita and Csaba Szepesvári · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J. Gordon, and J. Andrew Bagnell · 2011
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Exploration in model-based reinforcement learning by empirically estimating learning progress
Manuel Lopes, Tobias Lang, Marc Toussaint, and Pierre yves Oudeyer · 2012
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Near-optimal pac bounds for discounted mdps
Tor Lattimore and Marcus Hutter · 2014
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Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
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Convex optimization: Algorithms and complexity
Sébastien Bubeck et al · 2015
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Sample complexity of episodic fixed-horizon reinforcement learning
Christoph Dann and Emma Brunskill · 2015
Ex2: Exploration with exemplar models for deep reinforcement learning
Justin Fu, John Co-Reyes, and Sergey Levine · 2017
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Count-based exploration with neural density models
Georg Ostrovski, Marc G. Bellemare, Aäron van den Oord, and Rémi Munos · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
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#exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
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Imagination-augmented agents for deep reinforcement learning
Théophane Weber, Sébastien Racanière, David P Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomenech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, et al · 2017
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Variational information maximisation for intrinsically motivated reinforcement learning
Shakir Mohamed and Danilo Jimenez Rezende · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Unifying count-based exploration and intrinsic motivation
Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Rémi Munos · 2016
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Vime: Variational information maximizing exploration
Rein Houthooft, Xi Chen, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel · 2016
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Deep exploration via bootstrapped dqn
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
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Large-scale study of curiosity-driven learning
Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, and Alexei A. Efros · 2018
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2018
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Global convergence of policy gradient methods for the linear quadratic regulator
Maryam Fazel, Rong Ge, Sham Kakade, and Mehran Mesbahi · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Visual reinforcement learning with imagined goals
Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Randomized prior functions for deep reinforcement learning
Ian Osband, John Aslanides, and Albin Cassirer · 2018
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Episodic curiosity through reachability
Nikolay Savinov, Anton Raichuk, Raphaël Marinier, Damien Vincent, Marc Pollefeys, Timothy P. Lillicrap, and Sylvain Gelly · 2018
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Unsupervised control through non-parametric discriminative rewards
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, and Volodymyr Mnih · 2018
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On learning intrinsic rewards for policy gradient methods
Zeyu Zheng, Junhyuk Oh, and Satinder Singh · 2018
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