[1]ZHANG Wenxu,MA Lei,WANG Xiaodong.Reinforcement learning for event-triggered multi-agent systems[J].CAAI Transactions on Intelligent Systems,2017,12(1):82-87.[doi:10.11992/tis.201604008]
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Reinforcement learning for event-triggered multi-agent systems
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 1
Page number:
82-87
Column:
学术论文—机器学习
Public date:
2017-02-25
- Title:
- Reinforcement learning for event-triggered multi-agent systems
- Keywords:
- event-triggered; multi-agent; reinforcement learning; decentralized Markov decision processes; convergence
- CLC:
- TP181
- DOI:
- 10.11992/tis.201604008
- Abstract:
- Focusing on the existing multi-agent reinforcement learning problems such as huge consumption of communication and calculation, a novel event-triggered multi-agent reinforcement learning algorithm was presented. The algorithm focused on an event-triggered idea at the strategic level of multi-agent learning. In particular, during the interactive process between agents and the learning environment, the communication and learning were triggered through the change rate of observation.Using an appropriate event-triggered design, the discontinuous threshold was employed, and thus real-time or periodical communication and learning can be avoided, and the number of communications and calculations were reduced within the same time. Moreover, the consumption of computing resource and the convergence of the proposed algorithm were analyzed and proven. Finally, the simulation results show that the number of communications and traversals were reduced in learning, thus saving the computing and communication resources.
- References:
-
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Last Update:
1900-01-01