A NetHack Learning Environment Language Wrapper for Autonomous Agents
References
- Küttler H, Nardelli N, Miller A, Raileanu R, Selvatici M, Grefenstette E, Rocktäschel T.
The nethack learning environment . In Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H (eds.), Advances in Neural Information Processing Systems. 2020; 33: 7671–7684. Curran Associates, Inc. URLhttps://proceedings.neurips.cc/paper/2020/file/569ff987c643b4bedf504efda8f786c2-Paper.pdf . - Samvelyan M, Kirk R, Kurin V, Parker-Holder J, Jiang M, Hambro E, Petroni F, Kuttler H, Grefenstette E, Rocktäschel T. Minihack the planet: A sandbox for open-ended reinforcement learning research. In Vanschoren J, Yeung S (eds.), Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks. 2021; 1. URL
https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/fa7cdfad1a5aaf8370ebeda47a1ff1c3-Paper-round1.pdf . - Hambro E, Mohanty S, Babaev D, Byeon M, Chakraborty D, Grefenstette E, Jiang M, Daejin J, Kanervisto A, Kim J, Kim S, Kirk R, Kurin V, Küttler H, Kwon T, Lee D, Mella V, Nardelli N, Nazarov I, Ovsov N, Holder J, Raileanu R, Ramanauskas K, Rocktäschel T, Rothermel D, Samvelyan M, Sorokin D, Sypetkowski M, Sypetkowski M. Insights from the neurips 2021 nethack challenge. In Kiela D, Ciccone M, Caputo B (eds.), Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, volume 176 of Proceedings of Machine Learning Research.
06–14 Dec 2022 ; 41–52.PMLR . URLhttps://proceedings.mlr.press/v176/hambro22a.html . - Ruder S, Peters ME, Swayamdipta S, Wolf T. Transfer learning in natural language processing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials.
June 2019; 15–18 . Minneapolis, Minnesota:Association for Computational Linguistics . URLhttps://aclanthology.org/N19-5004 . DOI: 10.18653/v1/N19-5004 - Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler D, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D.
Language models are few-shot learners . In Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin, H (eds.), Advances in Neural Information Processing Systems. 2020; 33: 1877–1901. Curran Associates, Inc. URLhttps://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf . - Schick, T, Schütze H. It’s not just size that matters: Small language models are also few-shot learners. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
June 2021 ; 2339–2352. Online.Association for Computational Linguistics . URLhttps://aclanthology.org/2021.naacl-main.185 . DOI: 10.18653/v1/2021.naacl-main.185 - Reid M, Yamada Y, Gu SS. Can wikipedia help offline reinforcement learning? 2022. URL
https://arxiv.org/abs/2201.12122 . - Li S, Puig X, Paxton C, Du Y, Wang C, Fan L, Chen T, Huang D-A, Akyürek E, Anandkumar A, Andreas J, Mordatch I, Torralba A, Zhu Y. Pre-Trained Language Models for Interactive Decision-Making. arXiv e-prints, art. arXiv:2202.01771; February 2022.
- Hill F, Mokra S, Wong N, Harley T. Human instruction-following with deep reinforcement learning via transfer-learning from text; 2020. URL
https://arxiv.org/abs/2005.09382 . - Zhou X, Zhang Y, Cui L, Huang D. Evaluating commonsense in pre-trained language models; 2019. URL
https://arxiv.org/abs/1911.11931 . - Goodger N, Vamplew P, Foale C, Dazeley R. Language representations for generalization in reinforcement learning. In Vineeth NB and Ivor T, (eds.), Proceedings of The 13th Asian Conference on Machine Learning.
2021 ; 157: 390–405. Virtual. - Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Ukasz Kaiser Ł, Polosukhin I.
Attention is all you need . In Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds.), Advances in Neural Information Processing Systems. 2017; 30. Curran Associates, Inc. URLhttps://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf . - Narasimhan K, Kulkarni T, Barzilay R. Language understanding for text-based games using deep reinforcement learning. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.
September 2015; 1–11 . Lisbon, Portugal:Association for Computational Linguistics . DOI: 10.18653/v1/D15-1001 - He J, Chen J, He X, Gao J, Li L, Deng L, Ostendorf M. Deep reinforcement learning with a natural language action space. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
August 2016 ; 1621–1630. Berlin, Germany.Association for Computational Linguistics . URLhttps://aclanthology.org/P16-1153 . DOI: 10.18653/v1/P16-1153 - Côté M-A, Kádár Á, Yuan X, Kybartas B, Barnes T, Fine E, Moore J, Tao RY, Hausknecht M, El Asri L, Adada M, Tay W, Trischler A. Textworld: A learning environment for text-based games. CoRR, abs/1806.11532; 2018. DOI: 10.1007/978-3-030-24337-1_3
- Jansen PA, Côté M. Textworldexpress: Simulating text games at one million steps per second. CoRR, abs/2208.01174; 2022. DOI: 10.48550/arXiv.2208.01174
- Jiang M, Luketina J, Nardelli N, Minervini P, Torr PHS, Whiteson S, Rocktäschel T. Wordcraft: An environment for benchmarking commonsense agents. In Workshop on Language in Reinforcement Learning (LaRel); 2020. URL
https://github.com/minqi/wordcraft . - Koch K, McLean J, Segev R, Freed M, Berry
II M, Balasubramanian V, Sterling P. How much the eye tells the brain. Current biology: CB. 08 2006; 16: 1428–34. DOI: 10.1016/j.cub.2006.05.056 - Petrenko A, Huang Z, Kumar T, Sukhatme G, Koltun V. Sample factory: Egocentric 3d control from pixels at 100000 fps with asynchronous reinforcement learning. In ICML; 2020. DOI: 10.1016/j.cub.2006.05.056
- Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M, Davison J, Shleifer S, von Platen P, Ma C, Jernite Y, Plu J, Xu C, Le Scao T, Gugger S, Drame M, Lhoest Q, Alexander M. Rush. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations.
October 2020 ; 38–45. Online.Association for Computational Linguistics . URLhttps://www.aclweb.org/anthology/2020.emnlp-demos.6 . DOI: 10.18653/v1/2020.emnlp-demos.6
DOI: https://doi.org/10.5334/jors.444 | Journal eISSN: 2049-9647
Language: English
Submitted on: Oct 25, 2022
Accepted on: Mar 16, 2023
Published on: Jun 13, 2023
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2023 Nikolaj Goodger, Peter Vamplew, Cameron Foale, Richard Dazeley, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.