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Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents.
Nail: A general interactive fiction agent
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Feudal reinforcement learning
Peter Dayan and Geoffrey E Hinton. 1992 · 1992
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Learning to achieve goals
Leslie Pack Kaelbling. 1993 · 1993
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh. 1999 · 1999
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How to avoid being eaten by a grue: Exploration strategies for text-adventure agents
Prithviraj Ammanabrolu, Ethan Tien, Zhaochen Luo, and Mark O Riedl. 2020 · 2002
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Enhancing text-based reinforcement learning agents with commonsense knowledge
Keerthiram Murugesan, Mattia Atzeni, Pushkar Shukla, Mrinmaya Sachan, Pavan Kapanipathi, and Kartik Talamadupula. 2020 · 2005
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Curriculum learning
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Bebold: Exploration beyond the boundary of explored regions
Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E Gonzalez, and Yuandong Tian. 2020 · 2012
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Language understanding for text-based games using deep reinforcement learning
Karthik Narasimhan, Tejas D Kulkarni, and Regina Barzilay. 2015 · 2015
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Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2015 · 2015
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Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos. 2016 · 2016
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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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Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
Tejas D Kulkarni, Karthik Narasimhan, Ardavan Saeedi, and Josh Tenenbaum. 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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Modular multitask reinforcement learning with policy sketches
Jacob Andreas, Dan Klein, and Sergey Levine. 2017 · 2017
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba. 2017 · 2017
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Learning how to active learn: A deep reinforcement learning approach
Meng Fang, Yuan Li, and Trevor Cohn. 2017 · 2017
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Zero-shot task generalization with multi-task deep reinforcement learning
Junhyuk Oh, Satinder Singh, Honglak Lee, and Pushmeet Kohli. 2017 · 2017
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Composite task-completion dialogue policy learning via hierarchical deep reinforcement learning
Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao, Asli Celikyilmaz, Sungjin Lee, and Kam-Fai Wong. 2017 · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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FeUdal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu. 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, Ruo Yu Tao, Matthew Hausknecht, Layla El Asri, Mahmoud Adada, Wendy Tay, and Adam Trischler. 2018 · 2018
Language as an abstraction for hierarchical deep reinforcement learning
Yiding Jiang, Shixiang Shane Gu, Kevin P Murphy, and Chelsea Finn. 2019 · 2019
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A survey of reinforcement learning informed by natural language
Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, and Tim Rocktäschel. 2019 · 2019
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Learning to speak and act in a fantasy text adventure game
Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, and Jason Weston. 2019 · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al. 2019 · 2019
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Comprehensible context-driven text game playing
Xusen Yin and Jonathan May. 2019 · 2019
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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Shane Gu, Honglak Lee, and Sergey Levine. 2018 · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018 · 2018
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Taco: Learning task decomposition via temporal alignment for control
Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter, Shimon Whiteson, and Ingmar Posner. 2018 · 2018
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Hierarchical and interpretable skill acquisition in multi-task reinforcement learning
Tianmin Shu, Caiming Xiong, and Richard Socher. 2018 · 2018
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Hierarchical reinforcement learning for zero-shot generalization with subtask dependencies
Sungryull Sohn, Junhyuk Oh, and Honglak Lee. 2018 · 2018
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Counting to explore and generalize in text-based games
Xingdi (Eric) Yuan, Marc-Alexandre Côté, Alessandro Sordoni, Romain Laroche, Remi Tachet des Combes, Matthew 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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Learning dynamic belief graphs to generalize on text-based games
Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikuláš Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, and Will Hamilton. 2020 · 2020
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Ledeepchef: Deep reinforcement learning agent for families of text-based games
Leonard Adolphs and Thomas Hofmann. 2020 · 2020
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Graph constrained reinforcement learning for natural language action spaces
Prithviraj Ammanabrolu and Matthew Hausknecht. 2020 · 2020
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Interactive fiction game playing as multi-paragraph reading comprehension with reinforcement learning
Xiaoxiao Guo, Mo Yu, Yupeng Gao, Chuang Gan, Murray Campbell, and Shiyu Chang. 2020 · 2020
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Interactive fiction games: A colossal adventure
Matthew Hausknecht, Prithviraj Ammanabrolu, Marc-Alexandre Côté, and Xingdi Yuan. 2020 · 2020
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Algorithmic improvements for deep reinforcement learning applied to interactive fiction
Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, and Marc G Bellemare. 2020 · 2020
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Hierarchical reinforcement learning for open-domain dialog
Abdelrhman Saleh, Natasha Jaques, Asma Ghandeharioun, Judy Shen, and Rosalind Picard. 2020 · 2020
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Keep CALM and explore: Language models for action generation in text-based games
Shunyu Yao, Rohan Rao, Matthew Hausknecht, and Karthik Narasimhan. 2020 · 2020
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Learning to generalize for sequential decision making
Xusen Yin, Ralph Weischedel, and Jonathan May. 2020 · 2020
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How to motivate your dragon: Teaching goal-driven agents to speak and act in fantasy worlds
Prithviraj Ammanabrolu, Jack Urbanek, Margaret Li, Arthur Szlam, Tim Rocktäschel, and Jason Weston. 2021 · 2021
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Text-based rl agents with commonsense knowledge: New challenges, environments and baselines
Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, and Murray Campbell. 2021 · 2021
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Deep reinforcement learning with double q-learning
Hado van Hasselt, Arthur Guez, and David Silver. 2016 · 2094
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