Fetching the paper…
Reading the bibliography…
Text-based games provide an interactive way to study natural language processing.
Nail: A general interactive fiction agent
Matthew Hausknecht, Ricky Loynd, Greg Yang, Adith Swaminathan, and Jason D Williams. 2019 · 1902
Earlier work this paper cites.
Feudal reinforcement learning
Peter Dayan and Geoffrey E Hinton. 1992 · 1992
Earlier work this paper cites.
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
Earlier work this paper cites.
A review on multi-label learning algorithms
Min-Ling Zhang and Zhi-Hua Zhou. 2013 · 2013
Earlier work this paper cites.
Model regularization for stable sample rollouts
Erik Talvitie. 2014 · 2014
Earlier work this paper cites.
Deep reinforcement learning in large discrete action spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, and Ben Coppin. 2015 · 2015
Earlier work this paper cites.
Language understanding for text-based games using deep reinforcement learning
Karthik Narasimhan, Tejas D Kulkarni, and Regina Barzilay. 2015 · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
Tejas D Kulkarni, Karthik Narasimhan, Ardavan Saeedi, and Josh Tenenbaum. 2016 · 2016
Earlier work this paper cites.
Modular multitask reinforcement learning with policy sketches
Jacob Andreas, Dan Klein, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Accurately and efficiently interpreting human-robot instructions of varying granularities
Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson LS Wong, and Stefanie Tellex. 2017 · 2017
Earlier work this paper cites.
Learning how to active learn: A deep reinforcement learning approach
Meng Fang, Yuan Li, and Trevor Cohn. 2017 · 2017
Earlier work this paper cites.
Advances in pre-training distributed word representations
Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, and Armand Joulin. 2017 · 2017
Earlier work this paper cites.
Zero-shot task generalization with multi-task deep reinforcement learning
Junhyuk Oh, Satinder Singh, Honglak Lee, and Pushmeet Kohli. 2017 · 2017
Earlier work this paper cites.
Imagination-augmented agents for deep reinforcement learning
Sébastien Racanière, Théophane Weber, David P Reichert, Lars Buesing, Arthur Guez, Danilo Rezende, Adria Puigdomenech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, et al. 2017 · 2017
Earlier work this paper cites.
Sample-efficient actor-critic reinforcement learning with supervised data for dialogue management
Pei-Hao Su, Paweł Budzianowski, Stefan Ultes, Milica Gasic, and Steve Young. 2017 · 2017
Earlier work this paper cites.
Human learning in atari
Pedro A Tsividis, Thomas Pouncy, Jaqueline L Xu, Joshua B Tenenbaum, and Samuel J Gershman. 2017 · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
FeUdal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu. 2017 · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Investigating human priors for playing video games
Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, and Alexei Efros. 2018 · 2018
Earlier work this paper cites.
Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine. 2018 · 2018
Earlier work this paper cites.
Deep q-learning from demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, et al. 2018 · 2018
Earlier work this paper cites.
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
Cited alongside, same era.
Taco: Learning task decomposition via temporal alignment for control
Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter, Shimon Whiteson, and Ingmar Posner. 2018 · 2018
Cited alongside, same era.
Hierarchical reinforcement learning for zero-shot generalization with subtask dependencies
Sungryull Sohn, Junhyuk Oh, and Honglak Lee. 2018 · 2018
Cited alongside, same era.
Towards solving text-based games by producing adaptive action spaces
Ruo Yu Tao, Marc-Alexandre Côté, Xingdi Yuan, and Layla El Asri. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Lifelong learning with a changing action set
Yash Chandak, Georgios Theocharous, Chris Nota, and Philip Thomas. 2020 · 2020
Later among the works it cites.
Language as a cognitive tool to imagine goals in curiosity driven exploration
Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Dominey, and Pierre-Yves Oudeyer. 2020 · 2020
Later among the works it cites.
Probing emergent semantics in predictive agents via question answering
Abhishek Das, Federico Carnevale, Hamza Merzic, Laura Rimell, Rosalia Schneider, Josh Abramson, Alden Hung, Arun Ahuja, Stephen Clark, Greg Wayne, et al. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
Interactive fiction games: A colossal adventure
Matthew Hausknecht, Prithviraj Ammanabrolu, Marc-Alexandre Côté, and Xingdi Yuan. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Reinforcement and imitation learning for diverse visuomotor skills
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, and Nicolas Heess. 2018 · 2018
Cited alongside, same era.
Playing text-adventure games with graph-based deep reinforcement learning
Prithviraj Ammanabrolu and Mark Riedl. 2019 · 2019
Cited alongside, same era.
Learning to understand goal specifications by modelling reward
Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, and Edward Grefenstette. 2019 · 2019
Cited alongside, same era.
Learning action representations for reinforcement learning
Yash Chandak, Georgios Theocharous, James Kostas, Scott Jordan, and Philip Thomas. 2019 · 2019
Cited alongside, same era.
Fast task inference with variational intrinsic successor features
Steven Hansen, Will Dabney, Andre Barreto, David Warde-Farley, Tom Van de Wiele, and Volodymyr Mnih. 2019 · 2019
Cited alongside, same era.
Hierarchical decision making by generating and following natural language instructions
Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, and Mike Lewis. 2019 · 2019
Cited alongside, same era.
Algorithmic improvements for deep reinforcement learning applied to interactive fiction
Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, and Marc G Bellemare. 2020 · 2020
Later among the works it cites.
Skew-fit: State-covering self-supervised reinforcement learning
Vitchyr Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine. 2020 · 2020
Later among the works it cites.
Keep CALM and explore: Language models for action generation in text-based games
Shunyu Yao, Rohan Rao, Matthew Hausknecht, and Karthik Narasimhan. 2020 · 2020
Later among the works it cites.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
Ask your humans: Using human instructions to improve generalization in reinforcement learning
Valerie Chen, Abhinav Gupta, and Kenneth Marino. 2021 · 2021
Later among the works it cites.
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
Gabriel Dulac-Arnold, Nir Levine, Daniel J Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, and Todd Hester. 2021 · 2021
Later among the works it cites.
Hierarchical skills for efficient exploration
Jonas Gehring, Gabriel Synnaeve, Andreas Krause, and Nicolas Usunier. 2021 · 2021
Later among the works it cites.
Grounded language learning fast and slow
Felix Hill, Olivier Tieleman, Tamara von Glehn, Nathaniel Wong, Hamza Merzic, and Stephen Clark. 2021 · 2021
Later among the works it cites.
Language-based general action template for reinforcement learning agents
Ryosuke Kohita, Akifumi Wachi, Daiki Kimura, Subhajit Chaudhury, Michiaki Tatsubori, and Asim Munawar. 2021 · 2021
Later among the works it cites.
From motor control to team play in simulated humanoid football
Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, SM Eslami, Daniel Hennes, Wojciech M Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, et al. 2021 · 2021
Later among the works it cites.
Ella: Exploration through learned language abstraction
Suvir Mirchandani, Siddharth Karamcheti, and Dorsa Sadigh. 2021 · 2021
Later among the works it cites.
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
Later among the works it cites.
Pretraining representations for data-efficient reinforcement learning
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R Devon Hjelm, Philip Bachman, and Aaron C Courville. 2021 · 2021
Later among the works it cites.
Pre-trained language models as prior knowledge for playing text-based games
Ishika Singh, Gargi Singh, and Ashutosh Modi. 2021 · 2021
Later among the works it cites.
Generalization in text-based games via hierarchical reinforcement learning
Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, and Chengqi Zhang. 2021 · 2021
Later among the works it cites.
Data augmentation for graph neural networks
Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, and Neil Shah. 2021 · 2021
Later among the works it cites.