地域住民による自給自足型シェアサイクル運用システムの提案

—分散型ステーション強化学習を用いたシェアリングエコノミー—

抄録

In this research, we propose a new self-sufficient bicycle sharing operating system by local residents. The system incorporates Deep Q-Networks (DQN) at each station and uses a Multi-Agent Reinforcement Learning (MARL) model. In the proposed model, multiple reinforcement learning agents collaborate and provide incentives to local residents which can avoid making bicycles redistributed unevenly to bicycle stations. Additionally, we compare the simple method using the number of remaining bicycles, the MARL model, and a single-agent reinforcement learning (SARL) model to verify the improvement in learning speed and flexibility to changes in the environment. The simulation results show that the MARL model reduces the learning time due to the collaborative actions of multiple agents and results in more efficient service operations compared to the simple method and the SARL model.