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20212025
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.LG2023

Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation

Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami +3

Off-Policy Evaluation (OPE) aims to assess the effectiveness of counterfactual policies using only offline logged data and is often used to identify the top-k promising policies fo…

cs.LG2023

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

Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami +3

This paper introduces SCOPE-RL, a comprehensive open-source Python software designed for offline reinforcement learning (offline RL), off-policy evaluation (OPE), and selection (OP…