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Optimistic initialisation is an effective strategy for efficient exploration in reinforcement learning (RL).
A note on evaluating limits using riemann sums
Sudhir K Goel and Dennis M Rodriguez · 1987
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Self-improving reactive agents based on reinforcement learning, planning and teaching
Long-Ji Lin · 1992
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Summary cache: a scalable wide-area web cache sharing protocol
Li Fan, Pei Cao, Jussara Almeida, and Andrei Z Broder · 2000
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A bayesian framework for reinforcement learning
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R-MAX - a general polynomial time algorithm for near-optimal reinforcement learning
Ronen I Brafman and Moshe Tennenholtz · 2002
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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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An analysis of model-based interval estimation for markov decision processes
Alexander L Strehl and Michael L Littman · 2008
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What is intrinsic motivation? a typology of computational approaches
Pierre-Yves Oudeyer and Frederic Kaplan · 2009
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Near-optimal regret bounds for reinforcement learning
Thomas Jaksch, Ronald Ortner, and Peter Auer · 2010
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Domain-independent optimistic initialization for reinforcement learning
Marlos C Machado, Sriram Srinivasan, and Michael H Bowling · 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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Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 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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Conditional image generation with pixelcnn decoders
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Optimistic posterior sampling for reinforcement learning: worst-case regret bounds
Shipra Agrawal and Randy Jia · 2017
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Minimax regret bounds for reinforcement learning
Mohammad Gheshlaghi Azar, Ian Osband, and Rémi Munos · 2017
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2018
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Dora the explorer: Directed outreaching reinforcement action-selection
Leshem Choshen, Lior Fox, and Yonatan Loewenstein · 2018
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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
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Is 𝑸 \bm{Q} -learning provably efficient?
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, and Michael I Jordan · 2018
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Directed exploration in PAC model-free reinforcement learning
Min-hwan Oh and Garud Iyengar · 2018
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Richard Y Chen, John Schulman, Pieter Abbeel, and Szymon Sidor · 2017
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EX2: Exploration with exemplar models for deep reinforcement learning
Justin Fu, John Co-Reyes, and Sergey Levine · 2017
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Why is posterior sampling better than optimism for reinforcement learning?
Ian Osband and Benjamin Van Roy · 2017
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Deep exploration via randomized value functions
Ian Osband, Daniel Russo, Zheng Wen, and Benjamin Van Roy · 2017
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Count-based exploration with neural density models
Georg Ostrovski, Marc G Bellemare, Aaron 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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Randomized prior functions for deep reinforcement learning
Ian Osband, John Aslanides, and Albin Cassirer · 2018
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Approximate exploration through state abstraction
Adrien Ali Taïga, Aaron Courville, and Marc G Bellemare · 2018
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Contingency-aware exploration in reinforcement learning
Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh, Neal Wu, Mohammad Norouzi, and Honglak Lee · 2019
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𝑸 \bm{Q} -learning with UCB exploration is sample efficient for infinite-horizon MDP
Kefan Dong, Yuanhao Wang, Xiaoyu Chen, and Liwei Wang · 2019
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Go-explore: a new approach for hard-exploration problems
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Model-based active exploration
Pranav Shyam, Wojciech Jaśkowski, and Faustino Gomez · 2019
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