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Cooperative Multi-Robot Task Allocation with Reinforcement Learning
Content Provider | MDPI |
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Author | Park, Bumjin Kang, Cheongwoong Choi, Jaesik |
Copyright Year | 2021 |
Description | This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots or tasks increases. To tackle this problem, a novel Markov decision process formulation for multi-robot task allocation is presented for reinforcement learning. The proposed formulation sequentially allocates robots to tasks to minimize the total time taken to complete them. Additionally, we propose a deep reinforcement learning method to find the best allocation schedule for each problem. Our method adopts the cross-attention mechanism to compute the preference of robots to tasks. The experimental results show that the proposed method finds better solutions than meta-heuristic methods, especially when solving large-scale allocation problems. |
Starting Page | 272 |
e-ISSN | 20763417 |
DOI | 10.3390/app12010272 |
Journal | Applied Sciences |
Issue Number | 1 |
Volume Number | 12 |
Language | English |
Publisher | MDPI |
Publisher Date | 2021-12-28 |
Access Restriction | Open |
Subject Keyword | Applied Sciences Industrial Engineering Multi Robot Task Allocation Reinforcement Learning Deep Learning Artificial Intelligence |
Content Type | Text |
Resource Type | Article |