SCOPE-RL is a comprehensive open-source Python software that facilitates offline RL and OPE processes. It offers various OPE estimators, robust evaluation protocols, and user-friendly APIs. The library focuses on enhancing OPE by estimating reward distributions under policies rather than just expected values, providing a more thorough risk-return tradeoff assessment.
SCOPE-RL integrates both policy learning and evaluation aspects efficiently. It supports compatibility with Gym/Gymnasium environments and d3rlpy for implementing various offline RL methods. The library's documentation, visualization tools, and quickstart examples make it accessible to researchers and practitioners.
Key features of SCOPE-RL include end-to-end implementation of offline RL and OPE, a variety of OPE estimators, cumulative distribution OPE for risk function estimation, risk-return assessments in policy selection tasks, user-friendly APIs, visualization tools, and detailed documentation.
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by Haruka Kiyoh... klokken arxiv.org 03-12-2024
https://arxiv.org/pdf/2311.18206.pdfDypere Spørsmål