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Found 4 papers, 1 papers with code
ScanERU: Interactive 3D Visual Grounding based on Embodied Reference Understanding
Despite their effectiveness, existing methods suffer from the difficulty of low recognition accuracy in cases of multiple adjacent objects with similar appearances. To address this issue, this work intuitively introduces the human-robot interaction as a cue to facilitate the development of 3D visual grounding.
Dynamic Channel Access via Meta-Reinforcement Learning
In this paper, we address the channel access problem in a dynamic wireless environment via meta-reinforcement learning.
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access
We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously.
Actor-Critic Deep Reinforcement Learning for Dynamic Multichannel Access
We consider the dynamic multichannel access problem, which can be formulated as a partially observable Markov decision process (POMDP).
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