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

arXiv:2311.18206v3 Announce Type: replace
Abstract: 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 …

arxiv cs.ai cs.lg evaluation library offline policy python reinforcement reinforcement learning type