Distributed Reinforcement Learning based on α-domination Strategy and its Application to Shared Resource Problems
Kei AOKI, Kokolo IKEDA, Hajime KIMURA, Sigenobu KOBAYASHI
In the distributed systems in which information cannot be exchanged directly among agents, we deal with problems of deciding how each agent holds the shared resource. To achieve a lot of tasks greedily, agents tend to attempt to hold the resources for a long term. However the system performance decreases consequentially because it competes with the processing of other agents' tasks. To acquire cooperative policies that avoid above competition, we formulate the shared resource problems to multi-criteria decision making problems with the priority level by using the domain knowledge. We propose autonomous distributed control using distributed reinforcement learning that narrows the choice of action space by using the α-domination strategy based on value functions for the performance and the cooperation. The proposed method is applied to the distributed database systems, and simulation results shows that our method acquires cooperative policies and improves the throughput performance of the system.