most citedMUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning

6 citations · 15 across the 5 of their papers we have counts for

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cs.LG2022★ 1 cited

A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

Zihan Ding, Dijia Su, Qinghua Liu +1

This paper proposes new, end-to-end deep reinforcement learning algorithms for learning two-player zero-sum Markov games. Different from prior efforts on training agents to beat a…

cs.LG2021★ 6 cited

MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning

DiJia Su, Jason D. Lee, John M. Mulvey +1

In many contemporary applications such as healthcare, finance, robotics, and recommendation systems, continuous deployment of new policies for data collection and online learning i…