Dual deep Q-learning network guiding a multiagent path planning approach for virtual fire emergency scenarios

In this paper, we propose a novel framework for path planning in virtual emergency scenarios, which consists of three parts. (a) Configuration of the virtual environment: for convenience in handling, the virtual emergency scenario is discretized into many individual grid cells. (b)Policy generation: a dual deep Q-learning network approach is employed to obtain an effective policy that can alow agents to intelligently find effective paths. (c) Grouping strategy: a strategy is proposed to support multiple agents in achieving collective evacuation based on a given policy. Finally, extensive experiments are presented to validate the superiority of the proposed framework. The results show that by comparison with the existing related state-of-the-art methods, our proposed framework is superior and feasible.

Recommended citation: Wen Zhou, C. Zhang, S. Chen. (2023). Dual deep Q-learning network guiding a multiagent path planning approach for virtual fire emergency scenarios. Applied Intelligence. 53:21858–21874