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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.16382 |
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| _version_ | 1866929615930392576 |
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| author | Yuan, Mingqi Zhang, Zequn Xu, Yang Luo, Shihao Li, Bo Jin, Xin Zeng, Wenjun |
| author_facet | Yuan, Mingqi Zhang, Zequn Xu, Yang Luo, Shihao Li, Bo Jin, Xin Zeng, Wenjun |
| contents | We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms. More specifically, RLLTE decouples the RL algorithms completely from the exploitation-exploration perspective, providing a large number of components to accelerate algorithm development and evolution. In particular, RLLTE is the first RL framework to build a comprehensive ecosystem, which includes model training, evaluation, deployment, benchmark hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set standards for RL engineering practice and be highly stimulative for industry and academia. Our documentation, examples, and source code are available at https://github.com/RLE-Foundation/rllte. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_16382 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | RLLTE: Long-Term Evolution Project of Reinforcement Learning Yuan, Mingqi Zhang, Zequn Xu, Yang Luo, Shihao Li, Bo Jin, Xin Zeng, Wenjun Artificial Intelligence Machine Learning We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms. More specifically, RLLTE decouples the RL algorithms completely from the exploitation-exploration perspective, providing a large number of components to accelerate algorithm development and evolution. In particular, RLLTE is the first RL framework to build a comprehensive ecosystem, which includes model training, evaluation, deployment, benchmark hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set standards for RL engineering practice and be highly stimulative for industry and academia. Our documentation, examples, and source code are available at https://github.com/RLE-Foundation/rllte. |
| title | RLLTE: Long-Term Evolution Project of Reinforcement Learning |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2309.16382 |