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[1] Sergey Levine,et al. Latent Space Policies for Hierarchical Reinforcement Learning , 2018, ICML.
[2] Kenneth O. Stanley,et al. Go-Explore: a New Approach for Hard-Exploration Problems , 2019, ArXiv.
[3] Michael L. Littman,et al. Algorithms for Sequential Decision Making , 1996 .
[4] Alexei A. Efros,et al. Large-Scale Study of Curiosity-Driven Learning , 2018, ICLR.
[5] Michael P. Wellman,et al. Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm , 1998, ICML.
[6] Alex Graves,et al. Strategic Attentive Writer for Learning Macro-Actions , 2016, NIPS.
[7] Ludovic Denoyer,et al. Options Discovery with Budgeted Reinforcement Learning , 2016, ArXiv.
[8] Arjun K. Bansal,et al. Hierarchical Policy Learning is Sensitive to Goal Space Design , 2019, ArXiv.
[9] Sergey Levine,et al. Learning Actionable Representations with Goal-Conditioned Policies , 2018, ICLR.
[10] George Konidaris,et al. Discovering Options for Exploration by Minimizing Cover Time , 2019, ICML.
[11] Ruben Villegas,et al. Learning Latent Dynamics for Planning from Pixels , 2018, ICML.
[12] David M. Blei,et al. Topic segmentation with an aspect hidden Markov model , 2001, SIGIR '01.
[13] David M. Blei,et al. Variational Inference: A Review for Statisticians , 2016, ArXiv.
[14] Rémi Munos,et al. Neural Predictive Belief Representations , 2018, ArXiv.
[15] Sergey Levine,et al. High-Dimensional Continuous Control Using Generalized Advantage Estimation , 2015, ICLR.
[16] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[17] Jason M. O'Kane,et al. Active localization with dynamic obstacles , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[18] Peter I. Corke,et al. Visual Place Recognition: A Survey , 2016, IEEE Transactions on Robotics.
[19] Koray Kavukcuoglu,et al. Visual Attention , 2020, Computational Models for Cognitive Vision.
[20] Yuandong Tian,et al. Latent forward model for Real-time Strategy game planning with incomplete information , 2018 .
[21] Peter Stone,et al. Transfer Learning for Reinforcement Learning Domains: A Survey , 2009, J. Mach. Learn. Res..
[22] Yu Zhang,et al. Latent Sequence Decompositions , 2016, ICLR.
[23] N. Biggs. Algebraic Graph Theory: COLOURING PROBLEMS , 1974 .
[24] Christopher G. Atkeson,et al. Neural networks and differential dynamic programming for reinforcement learning problems , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[25] Dimitri P. Bertsekas,et al. Dynamic Programming and Optimal Control, Two Volume Set , 1995 .
[26] Zhengzhu Feng,et al. Dynamic Programming for Structured Continuous Markov Decision Problems , 2004, UAI.
[27] Sergey Levine,et al. Model-Based Reinforcement Learning for Atari , 2019, ICLR.
[28] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[29] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[30] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[31] Yoshua Bengio,et al. A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.
[32] Alexei A. Efros,et al. Curiosity-Driven Exploration by Self-Supervised Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[33] Jürgen Schmidhuber,et al. World Models , 2018, ArXiv.
[34] Bart De Schutter,et al. Multi-agent Reinforcement Learning: An Overview , 2010 .
[35] Ronald A. Howard,et al. Dynamic Programming and Markov Processes , 1960 .
[36] Philip Bachman,et al. Deep Reinforcement Learning that Matters , 2017, AAAI.
[37] Jiri Matas,et al. All you need is a good init , 2015, ICLR.
[38] Sergey Levine,et al. Data-Efficient Hierarchical Reinforcement Learning , 2018, NeurIPS.
[39] Sebastian Thrun,et al. Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..
[40] Pushmeet Kohli,et al. Compositional Imitation Learning: Explaining and executing one task at a time , 2018, ArXiv.
[41] Pieter Abbeel,et al. Apprenticeship learning via inverse reinforcement learning , 2004, ICML.
[42] Ion Stoica,et al. Multi-Level Discovery of Deep Options , 2017, ArXiv.
[43] Scott Niekum,et al. Incremental Semantically Grounded Learning from Demonstration , 2013, Robotics: Science and Systems.
[44] Max Welling,et al. Learning Sparse Neural Networks through L0 Regularization , 2017, ICLR.
[45] Sergey Levine,et al. Near-Optimal Representation Learning for Hierarchical Reinforcement Learning , 2018, ICLR.
[46] David K. Smith,et al. Dynamic Programming and Optimal Control. Volume 1 , 1996 .
[47] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[48] Leslie Pack Kaelbling,et al. Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..
[49] Yee Whye Teh,et al. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables , 2016, ICLR.
[50] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[51] Doina Precup,et al. The Option-Critic Architecture , 2016, AAAI.
[52] Max Welling,et al. Stochastic Activation Actor Critic Methods , 2019, ECML/PKDD.
[53] Demis Hassabis,et al. SCAN: Learning Abstract Hierarchical Compositional Visual Concepts , 2017, ArXiv.
[54] Geoffrey E. Hinton,et al. A Simple Way to Initialize Recurrent Networks of Rectified Linear Units , 2015, ArXiv.
[55] Razvan Pascanu,et al. Imagination-Augmented Agents for Deep Reinforcement Learning , 2017, NIPS.
[56] Bhaskara Marthi,et al. Concurrent Hierarchical Reinforcement Learning , 2005, IJCAI.
[57] Rémi Munos,et al. World Discovery Models , 2019, ArXiv.
[58] Horst Bischof,et al. Attentive Object Detection Using an Information Theoretic Saliency Measure , 2004, WAPCV.
[59] Sergey Levine,et al. Visual Reinforcement Learning with Imagined Goals , 2018, NeurIPS.
[60] Karol Gregor,et al. Temporal Difference Variational Auto-Encoder , 2018, ICLR.
[61] Maria Chatzigiorgaki,et al. Real-time keyframe extraction towards video content identification , 2009, 2009 16th International Conference on Digital Signal Processing.
[62] Marc G. Bellemare,et al. Count-Based Exploration with Neural Density Models , 2017, ICML.
[63] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[64] Yuandong Tian,et al. Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning , 2016, ICLR.
[65] Oliver Kroemer,et al. Towards learning hierarchical skills for multi-phase manipulation tasks , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[66] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[67] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[68] Jiebo Luo,et al. Image Captioning with Semantic Attention , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[69] Wolfram Burgard,et al. Active Markov localization for mobile robots , 1998, Robotics Auton. Syst..
[70] Eric T. Nalisnick,et al. Deep Generative Models with Stick-Breaking Priors , 2016 .
[71] Marco Cote. STICK-BREAKING VARIATIONAL AUTOENCODERS , 2017 .
[72] Mohamed Medhat Gaber,et al. Imitation Learning , 2017, ACM Comput. Surv..
[73] Benjamin Van Roy,et al. A Tutorial on Thompson Sampling , 2017, Found. Trends Mach. Learn..
[74] Karol Hausman,et al. Learning an Embedding Space for Transferable Robot Skills , 2018, ICLR.
[75] Wojciech Czarnecki,et al. Multi-task Deep Reinforcement Learning with PopArt , 2018, AAAI.
[76] S. Shankar Sastry,et al. Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning , 2017, ArXiv.
[77] Kate Saenko,et al. Hierarchical Actor-Critic , 2017, ArXiv.
[78] Alexei A. Efros,et al. Time-Agnostic Prediction: Predicting Predictable Video Frames , 2018, ICLR.
[79] Robert Babuska,et al. Actor-critic reinforcement learning for tracking control in robotics , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).
[80] Jean-Arcady Meyer,et al. Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words , 2008, IEEE Transactions on Robotics.
[81] Dimitri P. Bertsekas,et al. Nonlinear Programming , 1997 .
[82] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[83] Tom Schaul,et al. Successor Features for Transfer in Reinforcement Learning , 2016, NIPS.
[84] Joshua B. Tenenbaum,et al. Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation , 2016, NIPS.
[85] Geoffrey E. Hinton,et al. Feudal Reinforcement Learning , 1992, NIPS.
[86] Sergey Levine,et al. Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings , 2018, ICML.
[87] Richard S. Sutton,et al. Introduction to Reinforcement Learning , 1998 .
[88] Tom Schaul,et al. FeUdal Networks for Hierarchical Reinforcement Learning , 2017, ICML.
[89] Sergey Levine,et al. Continuous Deep Q-Learning with Model-based Acceleration , 2016, ICML.
[90] Elman Mansimov,et al. Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation , 2017, NIPS.
[91] Sergey Levine,et al. Search on the Replay Buffer: Bridging Planning and Reinforcement Learning , 2019, NeurIPS.
[92] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[93] Murray Shanahan,et al. SCAN: Learning Hierarchical Compositional Visual Concepts , 2017, ICLR.
[94] Ivan Titov,et al. Interpretable Neural Predictions with Differentiable Binary Variables , 2019, ACL.
[95] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.