Real-time contract-net-protocol scheduling model based on R-learning
Zhao Liang-hu · Computer Engineering and Applications Journal · 2014
This paper proposes a real-time scheduling model based on contract net protocol structure employing reinforcement learning agents. To this end, an R-learning procedure is elaborated and embedded in machine agents' decision process,enabling them to treat bid-invitations in more complicated way than in a simple contract net protocol environment. Efficiency of the proposed method is verified through experiments in a simulated real-time scheduling environment. Furthermore, the performance of mixed machine groups which comprises both reinforcement learning agents and non-reinforcement-learning agents shows that there is spontaneous implicit teamwork occurring between reinforcement learning agents, and this teamwork guarantees high quality output of the scheduling model.