Learning Skills in Reinforcement Learning Using Relative Novelty
暂无分享,去创建一个
[1] Doina Precup,et al. Temporal abstraction in reinforcement learning , 2000, ICML 2000.
[2] Bruce L. Digney,et al. Learning hierarchical control structures for multiple tasks and changing environments , 1998 .
[3] Shie Mannor,et al. Dynamic abstraction in reinforcement learning via clustering , 2004, ICML.
[4] Shie Mannor,et al. Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning , 2002, ECML.
[5] Tapio Elomaa,et al. Machine Learning: ECML 2002 , 2002, Lecture Notes in Computer Science.
[6] David G. Stork,et al. Pattern Classification , 1973 .
[7] Andrew G. Barto,et al. PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning , 2002, ICML.
[8] Peter Dayan,et al. Dopamine Bonuses , 2000, NIPS.
[9] Nuttapong Chentanez,et al. Intrinsically Motivated Learning of Hierarchical Collections of Skills , 2004 .
[10] R. W. White. Motivation reconsidered: the concept of competence. , 1959, Psychological review.
[11] Bernhard Hengst,et al. Discovering Hierarchy in Reinforcement Learning with HEXQ , 2002, ICML.
[12] Andrew G. Barto,et al. Using relative novelty to identify useful temporal abstractions in reinforcement learning , 2004, ICML.
[13] Sebastian Thrun,et al. Finding Structure in Reinforcement Learning , 1994, NIPS.
[14] Ronald E. Parr,et al. Hierarchical control and learning for markov decision processes , 1998 .
[15] Thomas G. Dietterich. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition , 1999, J. Artif. Intell. Res..
[16] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[17] Alicia P. Wolfe,et al. Identifying useful subgoals in reinforcement learning by local graph partitioning , 2005, ICML.
[18] Long Ji Lin,et al. Self-improving reactive agents based on reinforcement learning, planning and teaching , 1992, Machine Learning.
[19] Andrew G. Barto,et al. Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density , 2001, ICML.
[20] Andrew McCallum,et al. Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..