Evaluating skills in hierarchical reinforcement learning

[1]  Nasser Mozayani,et al.  Acquiring reusable skills in intrinsically motivated reinforcement learning , 2020, Journal of Intelligent Manufacturing.

[2]  Nasser Mozayani,et al.  Automatic construction and evaluation of macro-actions in reinforcement learning , 2019, Appl. Soft Comput..

[3]  Gabriel Dulac-Arnold,et al.  Challenges of Real-World Reinforcement Learning , 2019, ArXiv.

[4]  Peter Henderson,et al.  An Introduction to Deep Reinforcement Learning , 2018, Found. Trends Mach. Learn..

[5]  Gerald Tesauro,et al.  Learning Abstract Options , 2018, NeurIPS.

[6]  R. Socher,et al.  Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning , 2017, ICLR.

[7]  Gregory Dudek,et al.  Benchmark Environments for Multitask Learning in Continuous Domains , 2017, ArXiv.

[8]  John Thangarajah,et al.  Integrating Skills and Simulation to Solve Complex Navigation Tasks in Infinite Mario , 2017, IEEE Transactions on Games.

[9]  Ion Stoica,et al.  Multi-Level Discovery of Deep Options , 2017, ArXiv.

[10]  Marlos C. Machado,et al.  A Laplacian Framework for Option Discovery in Reinforcement Learning , 2017, ICML.

[11]  Masoud Asadpour,et al.  Graph based skill acquisition and transfer Learning for continuous reinforcement learning domains , 2017, Pattern Recognit. Lett..

[12]  Faruk Polat,et al.  Local Roots: A Tree-Based Subgoal Discovery Method to Accelerate Reinforcement Learning , 2016, ECML/PKDD.

[13]  Doina Precup,et al.  The Option-Critic Architecture , 2016, AAAI.

[14]  Filip De Turck,et al.  VIME: Variational Information Maximizing Exploration , 2016, NIPS.

[15]  Joshua B. Tenenbaum,et al.  Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation , 2016, NIPS.

[16]  Jan Hendrik Metzen,et al.  Learning Graph-Based Representations for Continuous Reinforcement Learning Domains , 2013, ECML/PKDD.

[17]  Peng Zhou,et al.  Discovering options from example trajectories , 2009, ICML '09.

[18]  Junichi Murata,et al.  Controlled Use of Subgoals in Reinforcement Learning , 2008 .

[19]  Thomas G. Dietterich,et al.  Automatic discovery and transfer of MAXQ hierarchies , 2008, ICML '08.

[20]  Peter Stone,et al.  The utility of temporal abstraction in reinforcement learning , 2008, AAMAS.

[21]  Kathryn E. Merrick,et al.  Modelling motivation for experience-based attention focus in reinforcement learning , 2007 .

[22]  Shie Mannor,et al.  Dynamic abstraction in reinforcement learning via clustering , 2004, ICML.

[23]  Shie Mannor,et al.  Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning , 2002, ECML.

[24]  Andrew G. Barto,et al.  Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density , 2001, ICML.

[25]  Thomas G. Dietterich An Overview of MAXQ Hierarchical Reinforcement Learning , 2000, SARA.

[26]  Doina Precup,et al.  Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..

[27]  Doina Precup,et al.  Intra-Option Learning about Temporally Abstract Actions , 1998, ICML.

[28]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[29]  Long Ji Lin,et al.  Self-improving reactive agents based on reinforcement learning, planning and teaching , 1992, Machine Learning.

[30]  Jan Hendrik Metzen,et al.  Learning the Structure of Continuous Markov Decision Processes , 2014 .

[31]  Wilco Moerman,et al.  Hierarchical Reinforcement Learning: Assignment of Behaviours to Subpolicies by Self-Organization , 2009 .

[32]  Andrew G. Barto,et al.  Behavioral building blocks for autonomous agents: description, identification, and learning , 2008 .

[33]  L. Sonenberg,et al.  Plans as a Means for Guiding a Reinforcement Learner , 2008 .

[34]  M. Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[36]  Nuttapong Chentanez,et al.  Intrinsically Motivated Learning of Hierarchical Collections of Skills , 2004 .

[37]  R. Sutton,et al.  Macro-Actions in Reinforcement Learning: An Empirical Analysis , 1998 .