Reinforced-lib: Rapid prototyping of reinforcement learning solutions
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2024
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| author | Szott, Szymon Wojnar, Maksymilian Rusek, Krzysztof Ciężobka, Wojciech |
| author_facet | Szott, Szymon Wojnar, Maksymilian Rusek, Krzysztof Ciężobka, Wojciech |
| contents | <p><strong>Introducing Reinforced-lib:</strong> a lightweight Python library for the rapid development of RL solutions. It is open-source, prioritizes ease of use, provides comprehensive documentation, and offers both deep reinforcement learning (DRL) and classic non-neural agents. Built on <a href="https://jax.readthedocs.io/en/latest/" rel="nofollow">JAX</a>, it facilitates exporting trained models to embedded devices, and makes it great for research and prototyping with RL algorithms. Access to JAX's just-in-time (JIT) compilation ensures high-performance results.<br><br><span>This research was funded by the National Science Centre, Poland (2020/39/I/ST7/01457) and by the German Research Foundation (DFG DR 639/28-1). We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2022/015838.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_11120099 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
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| spellingShingle | Reinforced-lib: Rapid prototyping of reinforcement learning solutions Szott, Szymon Wojnar, Maksymilian Rusek, Krzysztof Ciężobka, Wojciech Machine learning Wifi Reinforcement learning <p><strong>Introducing Reinforced-lib:</strong> a lightweight Python library for the rapid development of RL solutions. It is open-source, prioritizes ease of use, provides comprehensive documentation, and offers both deep reinforcement learning (DRL) and classic non-neural agents. Built on <a href="https://jax.readthedocs.io/en/latest/" rel="nofollow">JAX</a>, it facilitates exporting trained models to embedded devices, and makes it great for research and prototyping with RL algorithms. Access to JAX's just-in-time (JIT) compilation ensures high-performance results.<br><br><span>This research was funded by the National Science Centre, Poland (2020/39/I/ST7/01457) and by the German Research Foundation (DFG DR 639/28-1). We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2022/015838.</span></p> |
| title | Reinforced-lib: Rapid prototyping of reinforcement learning solutions |
| topic | Machine learning Wifi Reinforcement learning |
| url | https://doi.org/10.5281/zenodo.11120099 |