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most citedAccelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation

2 citations · 2 across the 4 of their papers we have counts for

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cs.LG2025

A General Framework for Off-Policy Learning with Partially-Observed Reward

Rikiya Takehi, Masahiro Asami, Kosuke Kawakami +1

Off-policy learning (OPL) in contextual bandits aims to learn a decision-making policy that maximizes the target rewards by using only historical interaction data collected under p…