32 citations · 74 across the 19 of their papers we have counts for
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cs.LG2020
Designing Interpretable Approximations to Deep Reinforcement Learning
Nathan Dahlin, Krishna Chaitanya Kalagarla, Nikhil Naik +2
In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance. However, depending upon additional constraints such a…
cs.LG2020★ 12 cited
A Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints
Krishna C. Kalagarla, Rahul Jain, Pierluigi Nuzzo
Constrained Markov Decision Processes (CMDPs) formalize sequential decision-making problems whose objective is to minimize a cost function while satisfying constraints on various c…