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Reward-free reinforcement learning (RL) is a framework which is suitable for both the batch RL setting and the setting where there are many reward functions of interest.
Provably efficient rl with rich observations via latent state decoding
Simon S Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, and John Langford · 1901
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Probabilistic computations: Toward a unified measure of complexity
Andrew Chi-Chin Yao · 1977
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Extensions of lipschitz mappings into a hilbert space
William B Johnson and Joram Lindenstrauss · 1984
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Neuro-dynamic programming , volume 5
Dimitri P Bertsekas and John N Tsitsiklis · 1996
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Constrained Markov decision processes , volume 7
Eitan Altman · 1999
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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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Simon S Du, Jason D Lee, Gaurav Mahajan, and Ruosong Wang · 2002
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Intrinsic motivation systems for autonomous mental development
Pierre-Yves Oudeyer, Frdric Kaplan, and Verena V Hafner · 2007
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Learning near-optimal policies with bellman-residual minimization based fitted policy iteration and a single sample path
András Antos, Csaba Szepesvári, and Rémi Munos · 2008
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Finite-time bounds for fitted value iteration
Rémi Munos and Csaba Szepesvári · 2008
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Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Jürgen Schmidhuber · 2010
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Online-to-confidence-set conversions and application to sparse stochastic bandits
Yasin Abbasi-Yadkori, David Pal, and Csaba Szepesvari · 2012
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Efficient exploration and value function generalization in deterministic systems
Zheng Wen and Benjamin Van Roy · 2013
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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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Vime: Variational information maximizing exploration
Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel · 2016
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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Automatic goal generation for reinforcement learning agents
Carlos Florensa, David Held, Xinyang Geng, and Pieter Abbeel · 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
Optimality and approximation with policy gradient methods in markov decision processes
Alekh Agarwal, Sham M Kakade, Jason D Lee, and Gaurav Mahajan · 2019
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Information-theoretic considerations in batch reinforcement learning
Jinglin Chen and Nan Jiang · 2019
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Provably efficient reinforcement learning with linear function approximation
Chi Jin, Zhuoran Yang, Zhaoran Wang, and Michael I Jordan · 2019
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Reinforcement learning with convex constraints
Sobhan Miryoosefi, Kianté Brantley, Hal Daume III, Miro Dudik, and Robert E Schapire · 2019
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Kinematic state abstraction and provably efficient rich-observation reinforcement learning
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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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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2018
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John D Co-Reyes, YuXuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, and Sergey Levine · 2018
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Curious: Intrinsically motivated multi-task multi-goal reinforcement learning
Cédric Colas, Pierre Fournier, Olivier Sigaud, and Pierre-Yves Oudeyer · 2018
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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Provably efficient maximum entropy exploration
Elad Hazan, Sham M Kakade, Karan Singh, and Abby Van Soest · 2018
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Visual reinforcement learning with imagined goals
Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, and John Langford · 2019
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Skew-fit: State-covering self-supervised reinforcement learning
Vitchyr H Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine · 2019
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Sample-optimal parametric q-learning using linearly additive features
Lin F. Yang and Mengdi Wang · 2019
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Frequentist regret bounds for randomized least-squares value iteration
Andrea Zanette, David Brandfonbrener, Matteo Pirotta, and Alessandro Lazaric · 2019
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Model-based reinforcement learning with value-targeted regression
Alex Ayoub, Zeyu Jia, Szepesvari Csaba, Mengdi Wang, and Lin F. Yang · 2020
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Provably efficient exploration in policy optimization
Qi Cai, Zhuoran Yang, Chi Jin, and Zhaoran Wang · 2020
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Reward-free exploration for reinforcement learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, and Tiancheng Yu · 2020
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Provably efficient reinforcement learning with general value function approximation
Ruosong Wang, Ruslan Salakhutdinov, and Lin F Yang · 2020
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