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Found 2 papers, 1 papers with code
SCALES: From Fairness Principles to Constrained Decision-Making
This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on the Constraint Markov Decision Process (CMDP).
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
The problem of inverse reinforcement learning (IRL) is relevant to a variety of tasks including value alignment and robot learning from demonstration.
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