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Found 3 papers, 2 papers with code
Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
Existing evaluation metrics for OPE estimators primarily focus on the "accuracy" of OPE or that of downstream policy selection, neglecting risk-return tradeoff in the subsequent online policy deployment.
SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation
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).
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation
In this position paper, we explore the potential of using simulation to accelerate practical research of offline RL and OPE, particularly in RecSys and RTB.
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