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20172023
most citedLearning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning

16 citations · 47 across the 11 of their papers we have counts for

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cs.MA2017

AUPCR Maximizing Matchings : Towards a Pragmatic Notion of Optimality for One-Sided Preference Matchings

Girish Raguvir J, Rahul Ramesh, Sachin Sridhar +1

We consider the problem of computing a matching in a bipartite graph in the presence of one-sided preferences. There are several well studied notions of optimality which include pa…

cs.LG2017★ 16 cited

Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning

Sahil Sharma, Aravind Suresh, Rahul Ramesh +1

Deep Reinforcement Learning (DRL) methods have performed well in an increasing numbering of high-dimensional visual decision making domains. Among all such visual decision making p…