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This paper introduces SCOPE-RL, a comprehensive open-source Python softwaredesigned for offline reinforcement learning (offline RL), off-policy evaluation(OPE), and selection (OPS). Unlike most existing libraries that focus solely oneither policy learning or evaluation, SCOPE-RL seamlessly integrates these twokey aspects, facilitating flexible and complete implementations of both offlineRL and OPE processes. SCOPE-RL put particular emphasis on its OPE modules,offering a range of OPE estimators and robust evaluation-of-OPE protocols. Thisapproach enables more in-depth and reliable OPE compared to other packages. Forinstance, SCOPE-RL enhances OPE by estimating the entire reward distributionunder a policy rather than its mere point-wise expected value. Additionally,SCOPE-RL provides a more thorough evaluation-of-OPE by presenting therisk-return tradeoff in OPE results, extending beyond mere accuracy evaluationsin existing OPE literature. SCOPE-RL is designed with user accessibility inmind. Its user-friendly APIs, comprehensive documentation, and a variety ofeasy-to-follow examples assist researchers and practitioners in efficientlyimplementing and experimenting with various offline RL methods and OPEestimators, tailored to their specific problem contexts. The documentation ofSCOPE-RL is available at https://scope-rl.readthedocs.io/en/latest/.

SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation