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Abstraction is crucial for effective sequential decision making in domains with large state spaces.
MinAtar: An Atari-inspired Testbed for More Efficient Reinforcement Learning Experiments
Young, K.; and Tian, T. 2019 · 1903
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Scalable methods for computing state similarity in deterministic Markov Decision Processes
Castro, P. S. 2019 · 1911
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A Markovian Decision Process
Bellman, R. 1957 · 1957
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Model Minimization in Markov Decision Processes
Dean, T. L.; and Givan, R. 1997 · 1997
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Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes
Dean, T. L.; Givan, R.; and Leach, S. M. 1997 · 1997
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The Information Bottleneck Method
Tishby, N.; Pereira, F.; and Bialek, W. 2001 · 2001
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Equivalence notions and model minimization in Markov decision processes
Givan, R.; Dean, T.; and Greig, M. 2003 · 2003
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Metrics for Finite Markov Decision Processes
Ferns, N.; Panangaden, P.; and Precup, D. 2004 · 2004
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An Algebraic Approach to Abstraction in Reinforcement Learning
Ravindran, B. 2004 · 2004
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Towards a Unified Theory of State Abstraction for MDPs
Li, L.; Walsh, T. J.; and Littman, M. L. 2006 · 2006
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Using Bisimulation for Policy Transfer in MDPs
Castro, P. S.; and Precup, D. 2010 · 2010
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Automatic Construction of Temporally Extended Actions for MDPs Using Bisimulation Metrics
Castro, P. S.; and Precup, D. 2011 · 2011
Cited alongside, same era.
Information Theory of Decisions and Actions , 601–636
Tishby, N.; and Polani, D. 2011 · 2011
Cited alongside, same era.
Trading Value and Information in MDPs , 57–74
Rubin, J.; Shamir, O.; and Tishby, N. 2012 · 2012
Cited alongside, same era.
Auto-Encoding Variational Bayes
Kingma, D. P.; and Welling, M. 2014 · 2014
Cited alongside, same era.
Human-level control through deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G.; Petersen, S.; Beattie, C.; Sadik, A.; Antonoglou, I.; King, H.; Kumaran, D.; Wierstra, D.; Legg, S.; and Hassabis, D. 2015 · 2015
Cited alongside, same era.
Near Optimal Behavior via Approximate State Abstraction
Abel, D.; Hershkowitz, D. E.; and Littman, M. L. 2016 · 2016
Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation
Corneil, D. S.; Gerstner, W.; and Brea, J. 2018 · 2018
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Ha, D.; and Schmidhuber, J. 2018 · 2018
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Learning Plannable Representations with Causal InfoGAN
Kurutach, T.; Tamar, A.; Yang, G.; Russell, S. J.; and Abbeel, P. 2018 · 2018
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Transfer with Model Features in Reinforcement Learning
Lehnert, L.; and Littman, M. L. 2018 · 2018
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The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Serban, I. V.; Sankar, C.; Pieper, M.; Pineau, J.; and Bengio, Y. 2018 · 2018
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Cited alongside, same era.
Deep Variational Information Bottleneck
Alemi, A. A.; Fischer, I.; Dillon, J. V.; and Murphy, K. 2017 · 2017
Cited alongside, same era.
Categorical Reparameterization with Gumbel-Softmax
Jang, E.; Gu, S.; and Poole, B. 2017 · 2017
Cited alongside, same era.
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Maddison, C. J.; Mnih, A.; and Teh, Y. W. 2017 · 2017
Cited alongside, same era.
Distral: Robust multitask reinforcement learning
Teh, Y. W.; Bapst, V.; Czarnecki, W.; Quan, J.; Kirkpatrick, J.; Hadsell, R.; Heess, N. M. O.; and Pascanu, R. 2017 · 2017
Cited alongside, same era.
Learning to Share and Hide Intentions using Information Regularization
Strouse, D.; Kleiman-Weiner, M.; Tenenbaum, J.; Botvinick, M.; and Schwab, D. J. 2018 · 2018
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State Abstraction as Compression in Apprenticeship Learning
Abel, D.; Arumugam, D.; Asadi, K.; Jinnai, Y.; Littman, M. L.; and Wong, L. L. S. 2019 · 2019
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Online Abstraction with MDP Homomorphisms for Deep Learning
Biza, O.; and Platt, R. 2019 · 2019
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Transfer and Exploration via the Information Bottleneck
Goyal, A.; Islam, R.; Strouse, D.; Ahmed, Z.; Larochelle, H.; Botvinick, M.; Levine, S.; and Bengio, Y. 2019 · 2019
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Plannable Approximations to MDP Homomorphisms: Equivariance under Actions
van der Pol, E.; Kipf, T.; Oliehoek, F. A.; and Welling, M. 2020 · 2020
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