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A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning.
Playing atari with deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; and Riedmiller, M. 2013 · 2013
Earlier work this paper cites.
Language Understanding for Text-based Games using Deep Reinforcement Learning
Narasimhan, K.; Kulkarni, T.; and Barzilay, R. 2015 · 2015
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Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
He, J.; Ostendorf, M.; He, X.; Chen, J.; Gao, J.; Li, L.; and Deng, L. 2016 · 2016
Earlier work this paper cites.
Textworld: A learning environment for text-based games
Côté, M.-A.; Kádár, A.; Yuan, X.; Kybartas, B.; Barnes, T.; Fine, E.; Moore, J.; Hausknecht, M.; El Asri, L.; Adada, M.; et al. 2018 · 2018
Earlier work this paper cites.
Reinforcement learning: An introduction
Sutton, R. S.; and Barto, A. G. 2018 · 2018
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Counting to explore and generalize in text-based games
Yuan, X.; Côté, M.-A.; Sordoni, A.; Laroche, R.; Combes, R. T. d.; Hausknecht, M.; and Trischler, A. 2018 · 2018
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NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language
Weber, L.; Minervini, P.; Münchmeyer, J.; Leser, U.; and Rocktäschel, T. 2019 · 2019
Earlier work this paper cites.
Comprehensible context-driven text game playing
Yin, X.; and May, J. 2019 · 2019
Earlier work this paper cites.
Learning dynamic belief graphs to generalize on text-based games
Adhikari, A.; Yuan, X.; Côté, M.-A.; Zelinka, M.; Rondeau, M.-A.; Laroche, R.; Poupart, P.; Tang, J.; Trischler, A.; and Hamilton, W. 2020 · 2020
Earlier work this paper cites.
Graph constrained reinforcement learning for natural language action spaces
Ammanabrolu, P.; and Hausknecht, M. 2020 · 2020
Earlier work this paper cites.
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 2020
Earlier work this paper cites.
Deep Learning For Symbolic Mathematics
Lample, G.; and Charton, F. 2020 · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
Earlier work this paper cites.
Deep reinforcement learning with stacked hierarchical attention for text-based games
Xu, Y.; Fang, M.; Chen, L.; Du, Y.; Zhou, J. T.; and Zhang, C. 2020 · 2020
Earlier work this paper cites.
Keep CALM and Explore: Language Models for Action Generation in Text-based Games
Yao, S.; Rao, R.; Hausknecht, M.; and Narasimhan, K. 2020 · 2020
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How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
Ammanabrolu, P.; Urbanek, J.; Li, M.; Szlam, A.; Rocktäschel, T.; and Weston, J. 2021 · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Chen, L.; Lu, K.; Rajeswaran, A.; Lee, K.; Grover, A.; Laskin, M.; Abbeel, P.; Srinivas, A.; and Mordatch, I. 2021 · 2021
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What would jiminy cricket do? Towards agents that behave morally
Hendrycks, D.; Mazeika, M.; Zou, A.; Patel, S.; Zhu, C.; Navarro, J.; Song, D.; Li, B.; and Steinhardt, J. 2021 · 2021
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Neuro-Symbolic Reinforcement Learning with First-Order Logic
Kimura, D.; Ono, M.; Chaudhury, S.; Kohita, R.; Wachi, A.; Agravante, D. J.; Tatsubori, M.; Munawar, A.; and Gray, A. 2021b · 2021
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Learning object-oriented dynamics for planning from text
Liu, G.; Adhikari, A.; Farahmand, A.-m.; and Poupart, P. 2021 · 2021
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ScienceWorld: Is your Agent Smarter than a 5th Grader?
Wang, R.; Jansen, P.; Côté, M.-A.; and Ammanabrolu, P. 2022a · 2022
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E. H.; Le, Q. V.; Zhou, D.; et al. 2022 · 2022
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Perceiving the World: Question-guided Reinforcement Learning for Text-based Games
Xu, Y.; Fang, M.; Chen, L.; Du, Y.; Zhou, J.; and Zhang, C. 2022 · 2022
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React: Synergizing reasoning and acting in language models
Yao, S.; Zhao, J.; Yu, D.; Du, N.; Shafran, I.; Narasimhan, K.; and Cao, Y. 2022 · 2022
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TextWorldExpress: Simulating Text Games at One Million Steps Per Second
Jansen, P.; and Cote, M.-a. 2023 · 2023
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Text-based rl agents with commonsense knowledge: New challenges, environments and baselines
Murugesan, K.; Atzeni, M.; Kapanipathi, P.; Shukla, P.; Kumaravel, S.; Tesauro, G.; Talamadupula, K.; Sachan, M.; and Campbell, M. 2021 · 2021
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Contrastive reinforcement learning of symbolic reasoning domains
Poesia, G.; Dong, W.; and Goodman, N. 2021 · 2021
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Generalization in Text-based Games via Hierarchical Reinforcement Learning
Xu, Y.; Fang, M.; Chen, L.; Du, Y.; and Zhang, C. 2021 · 2021
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A hybrid neuro-symbolic approach for text-based games using inductive logic programming
Basu, K.; Murugesan, K.; Atzeni, M.; Kapanipathi, P.; Talamadupula, K.; Klinger, T.; Campbell, M.; Sachan, M.; and Gupta, G. 2022 · 2022
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Brooks, E.; Walls, L.; Lewis, R. L.; and Singh, S. 2022 · 2022
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SenticNet 7: A commonsense-based neurosymbolic AI framework for explainable sentiment analysis
Cambria, E.; Liu, Q.; Decherchi, S.; Xing, F.; and Kwok, K. 2022 · 2022
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MetaICL: Learning to Learn In Context
Min, S.; Lewis, M.; Zettlemoyer, L.; and Hajishirzi, H. 2022 · 2022
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Reward design with language models
Kwon, M.; Xie, S. M.; Bullard, K.; and Sadigh, D. 2023 · 2023
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Code as policies: Language model programs for embodied control
Liang, J.; Huang, W.; Xia, F.; Xu, P.; Hausman, K.; Ichter, B.; Florence, P.; and Zeng, A. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Madaan, A.; Tandon, N.; Gupta, P.; Hallinan, S.; Gao, L.; Wiegreffe, S.; Alon, U.; Dziri, N.; Prabhumoye, S.; Yang, Y.; et al. 2023 · 2023
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OpenAI. 2023 · 2023
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Limitations of Language Models in Arithmetic and Symbolic Induction
Qian, J.; Wang, H.; Li, Z.; Li, S.; and Yan, X. 2023 · 2023
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Reflexion: an autonomous agent with dynamic memory and self-reflection
Shinn, N.; Labash, B.; and Gopinath, A. 2023 · 2023
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Progprompt: Generating situated robot task plans using large language models
Singh, I.; Blukis, V.; Mousavian, A.; Goyal, A.; Xu, D.; Tremblay, J.; Fox, D.; Thomason, J.; and Garg, A. 2023 · 2023
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ChatGPT for Robotics: Design Principles and Model Abilities
Vemprala, S.; Bonatti, R.; Bucker, A.; and Kapoor, A. 2023 · 2023
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Foundation models for decision making: Problems, methods, and opportunities
Yang, S.; Nachum, O.; Du, Y.; Wei, J.; Abbeel, P.; and Schuurmans, D. 2023 · 2023
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PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World
Zellers, R.; Holtzman, A.; Peters, M.; Mottaghi, R.; Kembhavi, A.; Farhadi, A.; and Choi, Y. 2021 · 2050
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