Jan 18, 2022 · In this paper, we propose a new deep reinforcement learning algorithm for multi-agent collaborative tasks with a variable number of agents. We ...
In this paper, we propose a new deep reinforcement learning algorithm for multi-agent collaborative tasks with a variable number of agents. We demonstrate the ...
Jan 18, 2022 · K-nearest Multi-agent Deep Reinforcement. Learning for Collaborative Tasks with a Variable. Number of Agents. Hamed Khorasgani. Hitachi ...
In this paper, we propose a new deep reinforcement learning algorithm for multi-agent collaborative tasks with a variable number of agents. We demonstrate the ...
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In this paper, we propose a new deep reinforcement learning algorithm for multi-agent collaborative tasks with a variable number of agents. We demonstrate the ...
K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents ... Traditionally, the performance of multi-agent deep ...
where K is the total number of agents (having NxK. machines in the system). Having multiple agents also has the additional. possibility of reduced observation ...
Missing: nearest Variable
Jan 18, 2022 · ... multi-agent deep reinforcement learning ... K-nearest ... learning algorithm for multi-agent collaborative tasks with a variable number of agents.
This paper introduces a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach for routing in Low Earth Orbit Satellite Constellations (LSatCs).
Based on this hypothesis, a method of hybrid joint / independent learning of MAS with a variable number of agents is proposed, which involves training a small ...