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We formalize the problem of selecting the optimal set of options for planning as that of computing the smallest set of options so that planning converges in less than a given maximum of value-iteration passes.
Models of man; social and rational
Simon, H. A · 1957
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A greedy heuristic for the set-covering problem
Chvatal, V · 1979
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The complexity of Markov decision processes
Papadimitriou, C. H. and Tsitsiklis, J. N · 1987
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A heuristic approach to the discovery of macro-operators
Iba, G. A · 1989
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The utility problem in case-based reasoning
Francis, A. G. and Ram, A · 1993
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Tight performance bounds on greedy policies based on imperfect value functions
Williams, R. J. and Baird, L. C · 1993
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Markov decision processes: discrete stochastic dynamic programming
Puterman, M · 1994
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On the complexity of solving Markov decision problems
Littman, M. L., Dean, T. L., and Kaelbling, L. P · 1995
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The complexity of plan existence and evaluation in probabilistic domains
Goldsmith, J., Littman, M. L., and Mundhenk, M · 1997
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Probabilistic propositional planning: Representations and complexity
Littman, M. L · 1997
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A sub-constant error-probability low-degree test, and a sub-constant error-probability pcp characterization of np
Raz, R. and Safra, S · 1997
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An O(log* n) approximation algorithm for the asymmetric p-center problem
Panigrahy, R. and Vishwanathan, S · 1998
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Multi-time models for temporally abstract planning
Precup, D. and Sutton, R. S · 1998
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Reinforcement Learning: An Introduction
Sutton, R. S. and Barto, A. G · 1998
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Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R., Precup, D., and Singh, S · 1999
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Two O(log* k)-approximation algorithms for the asymmetric k-center problem
Archer, A · 2001
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On the hardness of approximating spanners
Kortsarz, G · 2001
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Automatic discovery of subgoals in reinforcement learning using diverse density
McGovern, A. and Barto, A. G · 2001
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Q-cut - dynamic discovery of sub-goals in reinforcement learning
Menache, I., Mannor, S., and Shimkin, N · 2002
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Policyblocks: An algorithm for creating useful macro-actions in reinforcement learning
Pickett, M. and Barto, A · 2002
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Learning options in reinforcement learning
Stolle, M. and Precup, D · 2002
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Skill characterization based on betweenness
Şimşek, Ö. and Barto, A. G · 2009
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Transitive-closure spanners
Bhattacharyya, A., Grigorescu, E., Jung, K., Raskhodnikova, S., and Woodruff, D. P · 2012
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Label cover instances with large girth and the hardness of approximating basic k-spanner
Dinitz, M., Kortsarz, G., and Raz, R · 2012
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Automatic skill acquisition in reinforcement learning using graph centrality measures
Moradi, P., Shiri, M. E., Rad, A. A., Khadivi, A., and Hasler, M · 2012
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Compositional planning using optimal option models
Silver, D. and Ciosek, K · 2012
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On the bottleneck concept for options discovery
Bacon, P.-L · 2013
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Approximate equivalence of Markov decision processes
Even-Dar, E. and Mansour, Y · 2003
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Hierarchical reinforcement learning based on subgoal discovery and subpolicy specialization
Bakker, B. and Schmidhuber, J · 2004
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On the hardness of approximating label-cover
Dinur, I. and Safra, S · 2004
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Using relative novelty to identify useful temporal abstractions in reinforcement learning
Şimşek, Ö. and Barto, A · 2004
Cited alongside, same era.
Asymmetric k-center is log* n-hard to approximate
Chuzhoy, J., Guha, S., Halperin, E., Khanna, S., Kortsarz, G., Krauthgamer, R., and Naor, J. S · 2005
Cited alongside, same era.
Identifying useful subgoals in reinforcement learning by local graph partitioning
Şimşek, Ö., Wolfe, A., and Barto, A · 2005
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Analytical approach to parallel repetition
Dinur, I. and Steurer, D · 2014
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Scaling up approximate value iteration with options: Better policies with fewer iterations
Mann, T. and Mannor, S · 2014
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Approximate value iteration with temporally extended actions
Mann, T. A., Mannor, S., and Precup, D · 2015
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Constructing abstraction hierarchies using a skill-symbol loop
Konidaris, G · 2016
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When waiting is not an option: Learning options with a deliberation cost
Harb, J., Bacon, P.-L., Klissarov, M., and Precup, D · 2017
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A Laplacian framework for option discovery in reinforcement learning
Machado, M. C., Bellemare, M. G., and Bowling, M · 2017
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Diversity is all you need: Learning skills without a reward function
Eysenbach, B., Gupta, A., Ibarz, J., and Levine, S · 2018
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On value function representation of long horizon problems
Lehnert, L., Laroche, R., and van Seijen, H · 2018
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