Decision-making of Emergent Incident based on P-MADDPG

TL;DR

This paper introduces the P-MADDPG algorithm, which predicts incident nodes using GRU and enables faster emergency decision-making in multi-agent systems, outperforming existing algorithms in various scenarios.

Contribution

The paper proposes a novel P-MADDPG algorithm combining GRU prediction with multi-agent decision-making for emergent incidents, improving speed and accuracy.

Findings

P-MADDPG converges faster than MADDPG and greedy algorithms.

P-MADDPG performs better across different emergency scenarios.

Simulation results validate the effectiveness of the proposed method.

Abstract

In recent years, human casualties and damage to resources caused by emergent incidents have become a serious problem worldwide. In this paper, we model the emergency decision-making problem and use Multi-agent System (MAS) to solve the problem that the decision speed cannot keep up with the spreading speed. MAS can play an important role in the automated execution of these tasks to reduce mission completion time. In this paper, we propose a P-MADDPG algorithm to solve the emergency decision-making problem of emergent incidents, which predicts the nodes where an incident may occur in the next time by GRU model and makes decisions before the incident occurs, thus solving the problem that the decision speed cannot keep up with the spreading speed. A simulation environment was established for realistic scenarios, and three scenarios were selected to test the performance of P-MADDPG in emergency decision-making problems for emergent incidents: unmanned storage, factory assembly line, and civil airport baggage transportation. Simulation results using the P-MADDPG algorithm are compared with the greedy algorithm and the MADDPG algorithm, and the final experimental results show that the P-MADDPG algorithm converges faster and better than the other algorithms in scenarios of different sizes. This shows that the P-MADDP algorithm is effective for emergency decision-making in emergent incident.