IMPROVED PARTICLE SWAM OPTIMIZATION FOR CROWD SIMULATION USING HYBRID AGENT REINFORCEMENT LEARNING ALGORITHM

In an emergency route planning technique, simulating the dynamic crowd has route capacity constraints and global target of evacuating all crowd evacuees. To stimulate the crowd, the new arena is developed to know the real-time situation to face the crowd evacuation on exit point. The crowd evacuation is done with the process of Hybrid Agent Reinforcement Learning (HARL) algorithm consisting of Improved Multi-Agent Reinforcement Learning (IMARL) and State-Action-Reward-State-Action (SARSA). In the proposed work, the appropriate route selection mechanism focused on finding optimum evacuation route(s) is done in the first phase. Dynamic crowd can also be evacuated to find its way with the support of the HARL process in the second phase. The proposed HARL method can also be implemented with multi-objective improved particle swarm optimization (IPSO) technique for crowd simulation. The experimented results demonstrate the effectiveness of stability in the HARL process, which provides an improved performance for crowd simulation