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[1] Leslie Pack Kaelbling,et al. Input Generalization in Delayed Reinforcement Learning: An Algorithm and Performance Comparisons , 1991, IJCAI.
[2] Oliver Brock,et al. Learning state representations with robotic priors , 2015, Auton. Robots.
[3] Zhengzhu Feng,et al. Dynamic Programming for Structured Continuous Markov Decision Problems , 2004, UAI.
[4] Jon Louis Bentley,et al. An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1976, TOMS.
[5] Maximilian Karl,et al. Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data , 2016, ICLR.
[6] C A Nelson,et al. Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.
[7] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[8] S. Whiteson,et al. Adaptive Tile Coding for Value Function Approximation , 2007 .
[9] Katja Hofmann,et al. Meta Reinforcement Learning with Latent Variable Gaussian Processes , 2018, UAI.
[10] George Konidaris,et al. On the necessity of abstraction , 2019, Current Opinion in Behavioral Sciences.
[11] Andrew G. Barto,et al. Building Portable Options: Skill Transfer in Reinforcement Learning , 2007, IJCAI.
[12] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[13] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[14] Stuart J. Russell,et al. Reinforcement Learning with Hierarchies of Machines , 1997, NIPS.
[15] Shie Mannor,et al. A Deep Hierarchical Approach to Lifelong Learning in Minecraft , 2016, AAAI.
[16] Andrew McCallum,et al. Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks , 1996 .
[17] John McCarthy,et al. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955 , 2006, AI Mag..
[18] Manuela M. Veloso,et al. Tree Based Discretization for Continuous State Space Reinforcement Learning , 1998, AAAI/IAAI.
[19] R. A. Brooks,et al. Intelligence without Representation , 1991, Artif. Intell..
[20] Peter Stone,et al. Transfer Learning for Reinforcement Learning Domains: A Survey , 2009, J. Mach. Learn. Res..
[21] Henry Y. K. Lau,et al. Adaptive state space partitioning for reinforcement learning , 2004, Eng. Appl. Artif. Intell..
[22] Alan Fern,et al. Multi-task reinforcement learning: a hierarchical Bayesian approach , 2007, ICML '07.
[23] Andrew W. Moore,et al. Generalization in Reinforcement Learning: Safely Approximating the Value Function , 1994, NIPS.
[24] Lawson L. S. Wong,et al. State Abstraction as Compression in Apprenticeship Learning , 2019, AAAI.
[25] Tom Schaul,et al. Successor Features for Transfer in Reinforcement Learning , 2016, NIPS.
[26] Richard S. Sutton,et al. Learning to predict by the methods of temporal differences , 1988, Machine Learning.
[27] Maja J. Matarić,et al. Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks , 1996 .
[28] Andrew G. Barto,et al. Transfer in Reinforcement Learning via Shared Features , 2012, J. Mach. Learn. Res..
[29] Andrea Lockerd Thomaz,et al. Automatic task decomposition and state abstraction from demonstration , 2012, AAMAS.
[30] Thomas G. Dietterich. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition , 1999, J. Artif. Intell. Res..
[31] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[32] Zeb Kurth-Nelson,et al. Learning to reinforcement learn , 2016, CogSci.
[33] Ruslan Salakhutdinov,et al. Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning , 2015, ICLR.
[34] Thomas J. Walsh,et al. Towards a Unified Theory of State Abstraction for MDPs , 2006, AI&M.
[35] Yee Whye Teh,et al. Distral: Robust multitask reinforcement learning , 2017, NIPS.
[36] Michael L. Littman,et al. Near Optimal Behavior via Approximate State Abstraction , 2016, ICML.
[37] Michael L. Littman,et al. State Abstractions for Lifelong Reinforcement Learning , 2018, ICML.
[38] Ben J. A. Kröse,et al. Adaptive State Space Quantisation For Reinforcement Learning Of collision-free navigation , 1992, IROS.
[39] Peter L. Bartlett,et al. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , 2003, J. Mach. Learn. Res..
[40] Christopher Burgess,et al. DARLA: Improving Zero-Shot Transfer in Reinforcement Learning , 2017, ICML.
[41] Jason Pazis,et al. PAC Optimal Exploration in Continuous Space Markov Decision Processes , 2013, AAAI.
[42] Geoffrey E. Hinton,et al. Feudal Reinforcement Learning , 1992, NIPS.
[43] Peter Stone,et al. State Abstraction Synthesis for Discrete Models of Continuous Domains , 2018, AAAI Spring Symposia.
[44] Bruno Castro da Silva,et al. Learning Parameterized Skills , 2012, ICML.
[45] Thomas J. Walsh. Transferring State Abstractions Between MDPs , 2006 .
[46] Michael I. Jordan,et al. Reinforcement Learning with Soft State Aggregation , 1994, NIPS.
[47] Ameet Talwalkar,et al. Foundations of Machine Learning , 2012, Adaptive computation and machine learning.
[48] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[49] Sham M. Kakade,et al. On the sample complexity of reinforcement learning. , 2003 .
[50] Andrea Lockerd Thomaz,et al. Automatic State Abstraction from Demonstration , 2011, IJCAI.
[51] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[52] Marlos C. Machado,et al. State of the Art Control of Atari Games Using Shallow Reinforcement Learning , 2015, AAMAS.
[53] Marcus Hutter,et al. On Q-learning Convergence for Non-Markov Decision Processes , 2018, IJCAI.
[54] Gerald Tesauro,et al. Temporal Difference Learning and TD-Gammon , 1995, J. Int. Comput. Games Assoc..
[55] David Filliat,et al. State Representation Learning for Control: An Overview , 2018, Neural Networks.
[56] Andrew W. Moore,et al. The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces , 2004, Machine Learning.
[57] Satinder Singh,et al. Value Prediction Network , 2017, NIPS.
[58] Sriraam Natarajan,et al. Transfer in variable-reward hierarchical reinforcement learning , 2008, Machine Learning.
[59] Andrew G. Barto,et al. Autonomous shaping: knowledge transfer in reinforcement learning , 2006, ICML.
[60] Martin L. Puterman,et al. Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .
[61] Peter Dayan,et al. Q-learning , 1992, Machine Learning.
[62] Lihong Li,et al. PAC-inspired Option Discovery in Lifelong Reinforcement Learning , 2014, ICML.
[63] Andreas Maurer,et al. A Vector-Contraction Inequality for Rademacher Complexities , 2016, ALT.