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Reinforcement learning algorithms such as Q-learning have shown great promise in training models to learn the optimal action to take for a given system state; a goal in applications with an exploratory or adversarial nature such as task-oriented dialogues or games.
Action assembly: Sparse imitation learning for text based games with combinatorial action spaces
Chen Tessler, Tom Zahavy, Deborah Cohen, Daniel J. Mankowitz, and Shie Mannor. 2019 · 1905
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Signature verification using a” siamese” time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah. 1994 · 1994
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas. 2016 · 2003
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Visualizing data using t-SNE
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Playing atari with deep reinforcement learning
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Glove: Global vectors for word representation
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Unsupervised cross-domain transfer in policy gradient reinforcement learning via manifold alignment
Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, and Matthew E. Taylor. 2015 · 2015
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Siamese neural networks for one-shot image recognition
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Language understanding for text-based games using deep reinforcement learning
Karthik Narasimhan, Tejas Kulkarni, and Regina Barzilay. 2015 · 2015
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Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov. 2015 · 2015
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Deep reinforcement learning with a natural language action space
Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, and Mari Ostendorf. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 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 · 2016
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Successor features for transfer in reinforcement learning
Andre Barreto, Will Dabney, Remi Munos, Jonathan J Hunt, Tom Schaul, Hado P van Hasselt, and David Silver. 2017 · 2017
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What can you do with a rock? affordance extraction via word embeddings
Nancy Fulda, Daniel Ricks, Ben Murdoch, and David Wingate. 2017 · 2017
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Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, and Sergey Levine. 2017 · 2017
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Textworld: A learning environment for text-based games
Marc-Alexandre Côté, Ákos Kádár, Xingdi Yuan, Ben Kybartas, Tavian Barnes, Emery Fine, James Moore, Matthew J. Hausknecht, Layla El Asri, Mahmoud Adada, Wendy Tay, and Adam Trischler. 2018 · 2018
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Multi-task deep reinforcement learning with popart
Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, and Hado van Hasselt. 2018 · 2018
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Generating music medleys via playing music puzzle games
Yu-Siang Huang, Szu-Yu Chou, and Yi-Hsuan Yang. 2018 · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla. 2017 · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis. 2018 · 2018
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Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings
He He, Anusha Balakrishnan, Mihail Eric, and Percy Liang. 2017 · 2017
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Darla: Improving zero-shot transfer in reinforcement learning
Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, and Alexander Lerchner. 2017 · 2017
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Text-based adventures of the golovin AI agent
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Playing fps games with deep reinforcement learning
Guillaume Lample and Devendra Singh Chaplot. 2017 · 2017
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Deep transfer in reinforcement learning by language grounding
Karthik Narasimhan, Regina Barzilay, and Tommi S. Jaakkola. 2017 · 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 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Feature learning and transfer performance prediction for video reinforcement learning tasks via a siamese convolutional neural network
Jinhua Song, Yang Gao, and Hao Wang. 2018 · 2018
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Sample-efficient reinforcement learning through transfer and architectural priors
Benjamin Spector and Serge J. Belongie. 2018 · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto. 2018 · 2018
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Decipherment of historical manuscript images
Xusen Yin, Nada Aldarrab, Beáta Megyesi, and Kevin Knight. 2018 · 2018
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Counting to explore and generalize in text-based games
Xingdi Yuan, Marc-Alexandre Côté, Alessandro Sordoni, Romain Laroche, Remi Tachet des Combes, Matthew J. Hausknecht, and Adam Trischler. 2018 · 2018
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Learn what not to learn: Action elimination with deep reinforcement learning
Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, and Shie Mannor. 2018 · 2018
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Comprehensible context-driven text game playing
Xusen Yin and Jonathan May. 2019a · 2019
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Deep reinforcement learning with double q-learning
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