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We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards.
Playgol: learning programs through play
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Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
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Learning by experimentation: Incremental refinement of incomplete planning domains
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Autonomous Learning from the Environment
Shen, W.; and Simon, H. A. 1994 · 1994
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Inductive learning of reactive action models
Benson, S. 1995 · 1995
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Planning While Learning Operators
Wang, X. 1996 · 1996
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Top-down induction of first-order logical decision trees
Blockeel, H.; and De Raedt, L. 1998 · 1998
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Relational reinforcement learning
Džeroski, S.; De Raedt, L.; and Driessens, K. 2001 · 2001
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FF: The fast-forward planning system
Hoffmann, J. 2001 · 2001
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R-max-a general polynomial time algorithm for near-optimal reinforcement learning
Brafman, R. I.; and Tennenholtz, M. 2002 · 2002
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Near-optimal reinforcement learning in polynomial time
Kearns, M.; and Singh, S. 2002 · 2002
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The 3rd international planning competition: Results and analysis
Long, D.; and Fox, M. 2003 · 2003
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Relational reinforcement learning: An overview
Tadepalli, P.; Givan, R.; and Driessens, K. 2004 · 2004
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PPDDL1. 0: An extension to PDDL for expressing planning domains with probabilistic effects
Younes, H. L.; and Littman, M. L. 2004 · 2004
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The fast downward planning system
Helmert, M. 2006 · 2006
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Learning symbolic models of stochastic domains
Pasula, H. M.; Zettlemoyer, L. S.; and Kaelbling, L. P. 2007 · 2007
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FF-Replan: A baseline for probabilistic planning
Yoon, S.; Fern, A.; and Givan, R. 2007 · 2007
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International planning competition uncertainty part: Benchmarks and results
Bryce, D.; and Buffet, O. 2008 · 2008
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Exploiting open-endedness to solve problems through the search for novelty
Lehman, J.; and Stanley, K. O. 2008 · 2008
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A Comparison of h 2 and MMM for Mutex Pair Detection Applied to Pattern Databases
Sadeqi, M.; Holte, R. C.; and Zilles, S. 2014 · 2014
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Pybullet, a python module for physics simulation for games, robotics and machine learning
Coumans, E.; and Bai, Y. 2016 · 2016
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Modular active curiosity-driven discovery of tool use
Forestier, S.; and Oudeyer, P.-Y. 2016 · 2016
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Automatic goal generation for reinforcement learning agents
Florensa, C.; Held, D.; Geng, X.; and Abbeel, P. 2017 · 2017
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Intrinsically motivated goal exploration processes with automatic curriculum learning
Forestier, S.; Mollard, Y.; and Oudeyer, P.-Y. 2017 · 2017
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Goal babbling permits direct learning of inverse kinematics
Rolf, M.; Steil, J. J.; and Gienger, M. 2010 · 2010
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Efficient learning of relational models for sequential decision making
Walsh, T. J. 2010 · 2010
Cited alongside, same era.
Efficient Learning of Action Models for Planning
Mehta, N.; Tadepalli, P.; and Fern, A. 2011 · 2011
Cited alongside, same era.
Active learning of relational action models
Rodrigues, C.; Gérard, P.; Rouveirol, C.; and Soldano, H. 2011 · 2011
Cited alongside, same era.
Exploration in relational domains for model-based reinforcement learning
Lang, T.; Toussaint, M.; and Kersting, K. 2012 · 2012
Cited alongside, same era.
Active learning of inverse models with intrinsically motivated goal exploration in robots
Baranes, A.; and Oudeyer, P.-Y. 2013 · 2013
Cited alongside, same era.
A review of learning planning action models
Arora, A.; Fiorino, H.; Pellier, D.; Métivier, M.; and Pesty, S. 2018 · 2018
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Curiosity Driven Exploration of Learned Disentangled Goal Spaces
Laversanne-Finot, A.; Pere, A.; and Oudeyer, P.-Y. 2018 · 2018
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Visual reinforcement learning with imagined goals
Nair, A. V.; Pong, V.; Dalal, M.; Bahl, S.; Lin, S.; and Levine, S. 2018 · 2018
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Incremental learning of planning actions in model-based reinforcement learning
Ng, J. H. A.; and Petrick, R. 2019 · 2019
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Learning complex action models with quantifiers and logical implications
Zhuo, H. H.; Yang, Q.; Hu, D. H.; and Li, L. 2010 · 2019
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Learning with AMIGo: Adversarially Motivated Intrinsic Goals
Campero, A.; Raileanu, R.; Küttler, H.; Tenenbaum, J. B.; Rocktäschel, T.; and Grefenstette, E. 2020 · 2020
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Planning to Explore via Self-Supervised World Models
Sekar, R.; Rybkin, O.; Daniilidis, K.; Abbeel, P.; Hafner, D.; and Pathak, D. 2020 · 2020
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PDDLGym: Gym Environments from PDDL Problems
Silver, T.; and Chitnis, R. 2020 · 2020
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