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[1] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[2] W. Wonham,et al. The internal model principle for linear multivariable regulators , 1975 .
[3] Geoffrey E. Hinton,et al. Feudal Reinforcement Learning , 1992, NIPS.
[4] Long-Ji Lin,et al. Reinforcement learning for robots using neural networks , 1992 .
[5] Michael I. Jordan,et al. Reinforcement Learning with Soft State Aggregation , 1994, NIPS.
[6] Gerald Tesauro,et al. Temporal Difference Learning and TD-Gammon , 1995, J. Int. Comput. Games Assoc..
[7] Geoffrey J. Gordon. Stable Function Approximation in Dynamic Programming , 1995, ICML.
[8] Thomas Dean,et al. Decomposition Techniques for Planning in Stochastic Domains , 1995, IJCAI.
[9] Gerald Tesauro,et al. Temporal difference learning and TD-Gammon , 1995, CACM.
[10] John N. Tsitsiklis,et al. Analysis of Temporal-Diffference Learning with Function Approximation , 1996, NIPS.
[11] Milos Hauskrecht,et al. Hierarchical Solution of Markov Decision Processes using Macro-actions , 1998, UAI.
[12] Sebastian Thrun,et al. Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..
[13] Ronald Parr,et al. Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems , 1998, UAI.
[14] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[15] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[16] Thomas G. Dietterich. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition , 1999, J. Artif. Intell. Res..
[17] Shie Mannor,et al. Learning Embedded Maps of Markov Processes , 2001, ICML.
[18] Shie Mannor,et al. Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning , 2002, ECML.
[19] Eduardo D. Sontag,et al. Adaptation and regulation with signal detection implies internal model , 2003, Syst. Control. Lett..
[20] AUTOMATED DISCOVERY OF OPTIONS IN REINFORCEMENT LEARNING , 2003 .
[21] Shie Mannor,et al. Dynamic abstraction in reinforcement learning via clustering , 2004, ICML.
[22] Alicia P. Wolfe,et al. Identifying useful subgoals in reinforcement learning by local graph partitioning , 2005, ICML.
[23] Martin A. Riedmiller. Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method , 2005, ECML.
[24] C. Koch,et al. Invariant visual representation by single neurons in the human brain , 2005, Nature.
[25] Ulrike von Luxburg,et al. A tutorial on spectral clustering , 2007, Stat. Comput..
[26] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[27] Pascal Vincent,et al. Visualizing Higher-Layer Features of a Deep Network , 2009 .
[28] Benjamin Pitzer,et al. Towards perceptual shared autonomy for robotic mobile manipulation , 2011, 2011 IEEE International Conference on Robotics and Automation.
[29] Gaël Varoquaux,et al. Mayavi: 3D Visualization of Scientific Data , 2010, Computing in Science & Engineering.
[30] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[31] Shie Mannor,et al. Model selection in markovian processes , 2013, KDD.
[32] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[33] Laurens van der Maaten,et al. Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..
[34] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[35] Melba M. Crawford,et al. Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning , 2014, IEEE Signal Processing Magazine.
[36] Shie Mannor,et al. Time-regularized interrupting options , 2014, ICML 2014.
[37] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[38] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Shie Mannor,et al. Approximate Value Iteration with Temporally Extended Actions , 2015, J. Artif. Intell. Res..
[40] Shane Legg,et al. Massively Parallel Methods for Deep Reinforcement Learning , 2015, ArXiv.
[41] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[42] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[43] Marc G. Bellemare,et al. The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) , 2012, IJCAI.
[44] Ruslan Salakhutdinov,et al. Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning , 2015, ICLR.
[45] Stuart J. Russell,et al. Markovian State and Action Abstractions for MDPs via Hierarchical MCTS , 2016, IJCAI.
[46] David Silver,et al. Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.
[47] Tom Schaul,et al. Dueling Network Architectures for Deep Reinforcement Learning , 2015, ICML.
[48] Shie Mannor,et al. Spatio-Temporal Abstractions in Reinforcement Learning Through Neural Encoding , 2016 .
[49] Shie Mannor,et al. Visualizing Dynamics: from t-SNE to SEMI-MDPs , 2016, ArXiv.
[50] Sergey Levine,et al. End-to-End Training of Deep Visuomotor Policies , 2015, J. Mach. Learn. Res..
[51] Tom Schaul,et al. Prioritized Experience Replay , 2015, ICLR.
[52] Joshua B. Tenenbaum,et al. Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation , 2016, NIPS.
[53] Shie Mannor,et al. Adaptive Skills Adaptive Partitions (ASAP) , 2016, NIPS.
[54] Marc G. Bellemare,et al. Increasing the Action Gap: New Operators for Reinforcement Learning , 2015, AAAI.
[55] Shie Mannor,et al. A Deep Hierarchical Approach to Lifelong Learning in Minecraft , 2016, AAAI.