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