most citedEfficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization

4 citations · 6 across the 2 of their papers we have counts for

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cs.LG2022★ 2 cited

SCALES: From Fairness Principles to Constrained Decision-Making

Sreejith Balakrishnan, Jianxin Bi, Harold Soh

This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on the Constraint Markov Decision Process (C…

cs.LG2020★ 4 cited

Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization

Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low +1

The problem of inverse reinforcement learning (IRL) is relevant to a variety of tasks including value alignment and robot learning from demonstration. Despite significant algorithm…