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We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.
Self-improving reactive agents based on reinforcement learning, planning and teaching
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Learning to parse database queries using inductive logic programming
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Long short-term memory
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Actor-critic algorithms
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Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars
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Structured learning and prediction in computer vision
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A reduction of imitation learning and structured prediction to no-regret online learning
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Off-policy actor-critic
Thomas Degris, Martha White, and Richard S Sutton · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Reinforcement learning neural turing machines
Wojciech Zaremba and Ilya Sutskever · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Józefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Gordon Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Vime: Variational information maximizing exploration
Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel · 2016
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Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Learning a natural language interface with neural programmer
Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew D McCallum, and Dario Amodei · 2016
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Reward augmented maximum likelihood for neural structured prediction
Mohammad Norouzi, Samy Bengio, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans, et al · 2016
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Inferring logical forms from denotations
Panupong Pasupat and Percy Liang · 2016
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Inferring logical forms from denotations
Panupong Pasupat and Percy Liang · 2016
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2016
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Prioritized experience replay
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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#exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
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Sample efficient actor-critic with experience replay
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, and Nando de Freitas · 2017
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Macro grammars and holistic triggering for efficient semantic parsing
Yuchen Zhang, Panupong Pasupat, and Percy Liang · 2017
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher · 2017
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016
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The value of semantic parse labeling for knowledge base question answering
Wen-tau Yih, Matthew Richardson, Chris Meek, Ming-Wei Chang, and Jina Suh · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
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Deepcoder: Learning to write programs
M. Balog, A. L. Gaunt, M. Brockschmidt, S. Nowozin, and D. Tarlow · 2017
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2017
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Neural program synthesis with priority queue training
Daniel A Abolafia, Mohammad Norouzi, and Quoc V Le · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
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Neural multi-step reasoning for question answering on semi-structured tables
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Deep q-learning from demonstrations
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Natural language to structured query generation via meta-learning
Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen tau Yih, and Xiaodong He · 2018
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Simple random search provides a competitive approach to reinforcement learning
Horia Mania, Aurelia Guy, and Benjamin Recht · 2018
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It was the training data pruning too!
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Gotta learn fast: A new benchmark for generalization in rl
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Self-imitation learning
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Semantic parsing with syntax-and table-aware sql generation
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Pointing out SQL queries from text
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Variance reduction for policy gradient with action-dependent factorized baselines
Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M Bayen, Sham Kakade, Igor Mordatch, and Pieter Abbeel · 2018
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SQLNet: Generating structured queries from natural language without reinforcement learning
Xiaojun Xu, Chang Liu, and Dawn Song · 2018
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Typesql: Knowledge-based type-aware neural text-to-sql generation
Tao Yu, Zifan Li, Zilin Zhang, Rui Zhang, and Dragomir Radev · 2018
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