Search Results for author:
Found 3 papers, 2 papers with code
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB), where a policy selects a subset in the action space.
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).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
