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Collaborative Deep Reinforcement Learning for Joint Object Search
[article]
2017
arXiv
pre-print
We examine the problem of joint top-down active search of multiple objects under interaction, e.g., person riding a bicycle, cups held by the table, etc.. Such objects under interaction often can provide contextual cues to each other to facilitate more efficient search. By treating each detector as an agent, we present the first collaborative multi-agent deep reinforcement learning algorithm to learn the optimal policy for joint active object localization, which effectively exploits such
arXiv:1702.05573v1
fatcat:agtduezvm5bqlobykfk5c37k3a