K-nearest Multi-agent Deep Reinforcement Learning for Collaborative
Tasks with a Variable Number of Agents
- URL: http://arxiv.org/abs/2201.07092v1
- Date: Tue, 18 Jan 2022 16:14:24 GMT
- Title: K-nearest Multi-agent Deep Reinforcement Learning for Collaborative
Tasks with a Variable Number of Agents
- Authors: Hamed Khorasgani, Haiyan Wang, Hsiu-Khuern Tang, Chetan Gupta
- Abstract summary: We propose a new deep reinforcement learning algorithm for multi-agent collaborative tasks with a variable number of agents.
We demonstrate the application of our algorithm using a fleet management simulator developed by Hitachi to generate realistic scenarios in a production site.
- Score: 13.110291070230815
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Traditionally, the performance of multi-agent deep reinforcement learning
algorithms are demonstrated and validated in gaming environments where we often
have a fixed number of agents. In many industrial applications, the number of
available agents can change at any given day and even when the number of agents
is known ahead of time, it is common for an agent to break during the operation
and become unavailable for a period of time. 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 application of our algorithm
using a fleet management simulator developed by Hitachi to generate realistic
scenarios in a production site.
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