CompILE: Compositional Imitation Learning and Execution
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Pushmeet Kohli | Edward Grefenstette | Hanjun Dai | Thomas Kipf | Alvaro Sanchez-Gonzalez | Peter W. Battaglia | Yujia Li | Vinícius Flores Zambaldi | Pushmeet Kohli | P. Battaglia | V. Zambaldi | Yujia Li | Thomas Kipf | Edward Grefenstette | Alvaro Sanchez-Gonzalez | H. Dai
[1] D. Davidson. Inquiries Into Truth and Interpretation , 1984 .
[2] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[3] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[4] Jeffrey M. Zacks,et al. Perceiving, remembering, and communicating structure in events. , 2001, Journal of experimental psychology. General.
[5] David M. Blei,et al. Topic segmentation with an aspect hidden Markov model , 2001, SIGIR '01.
[6] T. Griffiths,et al. A Bayesian framework for word segmentation: Exploring the effects of context , 2009, Cognition.
[7] L. Davachi,et al. What Constitutes an Episode in Episodic Memory? , 2011, Psychological science.
[8] Alex Graves,et al. Supervised Sequence Labelling , 2012 .
[9] Scott Niekum,et al. Incremental Semantically Grounded Learning from Demonstration , 2013, Robotics: Science and Systems.
[10] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[11] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[12] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[13] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[14] Honglak Lee,et al. Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.
[15] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[16] Samy Bengio,et al. Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Oliver Kroemer,et al. Towards learning hierarchical skills for multi-phase manipulation tasks , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[18] Jakob Uszkoreit,et al. A Decomposable Attention Model for Natural Language Inference , 2016, EMNLP.
[19] Geoffrey E. Hinton,et al. Layer Normalization , 2016, ArXiv.
[20] Ryan P. Adams,et al. Composing graphical models with neural networks for structured representations and fast inference , 2016, NIPS.
[21] Kenneth A. Norman,et al. Discovering Event Structure in Continuous Narrative Perception and Memory , 2016, Neuron.
[22] Bernard Ghanem,et al. DAPs: Deep Action Proposals for Action Understanding , 2016, ECCV.
[23] Joshua B. Tenenbaum,et al. Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation , 2016, NIPS.
[24] Tom Schaul,et al. FeUdal Networks for Hierarchical Reinforcement Learning , 2017, ICML.
[25] Yu Zhang,et al. Latent Sequence Decompositions , 2016, ICLR.
[26] Ion Stoica,et al. Multi-Level Discovery of Deep Options , 2017, ArXiv.
[27] Doina Precup,et al. The Option-Critic Architecture , 2016, AAAI.
[28] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[29] Ben Poole,et al. Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.
[30] Jeffrey M. Zacks,et al. Event boundaries in memory and cognition , 2017, Current Opinion in Behavioral Sciences.
[31] Honglak Lee,et al. Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning , 2017, ICML.
[32] Dan Klein,et al. Modular Multitask Reinforcement Learning with Policy Sketches , 2016, ICML.
[33] Pieter Abbeel,et al. Stochastic Neural Networks for Hierarchical Reinforcement Learning , 2016, ICLR.
[34] Yee Whye Teh,et al. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables , 2016, ICLR.
[35] Chong Wang,et al. Sequence Modeling via Segmentations , 2017, ICML.
[36] Juan Carlos Niebles,et al. Dense-Captioning Events in Videos , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[37] Gaurav S. Sukhatme,et al. Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets , 2017, NIPS.
[38] Jeffrey M. Zacks,et al. Constructing Experience: Event Models from Perception to Action , 2017, Trends in Cognitive Sciences.
[39] Anca D. Dragan,et al. DART: Noise Injection for Robust Imitation Learning , 2017, CoRL.
[40] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[41] Le Song,et al. Recurrent Hidden Semi-Markov Model , 2017, ICLR.
[42] Ion Stoica,et al. DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations , 2017, CoRL.
[43] Gerald Tesauro,et al. Learning Abstract Options , 2018, NeurIPS.
[44] Shimon Whiteson,et al. TACO: Learning Task Decomposition via Temporal Alignment for Control , 2018, ICML.
[45] Chong Wang,et al. Subgoal Discovery for Hierarchical Dialogue Policy Learning , 2018, EMNLP.
[46] Shane Legg,et al. IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures , 2018, ICML.
[47] Stefan Bauer,et al. Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models , 2018, NeurIPS.
[48] Yuval Tassa,et al. DeepMind Control Suite , 2018, ArXiv.
[49] Hyeonwoo Noh,et al. Neural Program Synthesis from Diverse Demonstration Videos , 2018, ICML.
[50] Dawn Xiaodong Song,et al. Parametrized Hierarchical Procedures for Neural Programming , 2018, ICLR.
[51] Joseph J. Lim,et al. KeyIn: Discovering Subgoal Structure with Keyframe-based Video Prediction , 2019, ArXiv.
[52] Joseph J. Lim,et al. Composing Complex Skills by Learning Transition Policies , 2018, ICLR.
[53] Alexei A. Efros,et al. Time-Agnostic Prediction: Predicting Predictable Video Frames , 2018, ICLR.
[54] Mohit Sharma,et al. Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information , 2018, ICLR.