Reinforce-lib: A Reinforcement Learning Library for Scientific Research
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| Title: | Reinforce-lib: A Reinforcement Learning Library for Scientific Research |
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| Authors: | Anzalone, Luca, Bonacorsi, Daniele |
| Contributors: | Anzalone, Luca, Bonacorsi, Daniele |
| Publication Year: | 2022 |
| Collection: | IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) |
| Subject Terms: | Reinforcement learning, deep learning, python library |
| Description: | Reinforcement Learning (RL) has already achieved several breakthroughs on complex, high-dimensional, and even multi-agent tasks, gaining increasingly interest from not only the research community. Although very powerful in principle, its applicability is still limited to solving games and control problems, leaving plenty opportunities to apply and develop RL algorithms for (but not limited to) scientific domains like physics, and biology. Apart from the domain of interest, the applicability of RL is also limited by numerous difficulties encountered while training agents, like training instabilities and sensitivity to hyperparameters. For such reasons, we propose a modern, modular, simple and understandable Python RL library called reinforce-lib. Our main aim is to enable newcomers, practitioners, and researchers to easily employ RL to solve new scientific problems. Our library is available at https://github.com/Luca96/reinforce-lib. |
| Document Type: | conference object |
| File Description: | ELETTRONICO |
| Language: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/wos/WOS:001326815700014; ispartofbook:International Symposium on Grids & Clouds 2022 (ISGC2022); International Symposium on Grids & Clouds 2022 (ISGC2022); volume:415; firstpage:1; lastpage:17; numberofpages:17; journal:POS PROCEEDINGS OF SCIENCE; https://hdl.handle.net/11585/914565; https://pos.sissa.it/415/018 |
| DOI: | 10.22323/1.415.0018 |
| Availability: | https://hdl.handle.net/11585/914565 https://doi.org/10.22323/1.415.0018 https://pos.sissa.it/415/018 |
| Rights: | info:eu-repo/semantics/openAccess |
| Accession Number: | edsbas.8F1D261 |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | Reinforcement Learning (RL) has already achieved several breakthroughs on complex, high-dimensional, and even multi-agent tasks, gaining increasingly interest from not only the research community. Although very powerful in principle, its applicability is still limited to solving games and control problems, leaving plenty opportunities to apply and develop RL algorithms for (but not limited to) scientific domains like physics, and biology. Apart from the domain of interest, the applicability of RL is also limited by numerous difficulties encountered while training agents, like training instabilities and sensitivity to hyperparameters. For such reasons, we propose a modern, modular, simple and understandable Python RL library called reinforce-lib. Our main aim is to enable newcomers, practitioners, and researchers to easily employ RL to solve new scientific problems. Our library is available at https://github.com/Luca96/reinforce-lib. |
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| DOI: | 10.22323/1.415.0018 |