Multi-Agent Reinforcement Learning-Based Control Method for Pedestrian Guidance Using the Mojiko Fireworks Festival Dataset

With increasing incidents due to congestion at events, effective pedestrian guidance has become a critical safety concern. Recent research has explored the application of reinforcement learning to crowd simulation, where agents learn optimal actions through trial and error to maximize rewards based on environmental states. This study investigates the use of reinforcement learning and simulation techniques to mitigate pedestrian congestion through improved guidance systems. We employ the Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG), a multi-agent reinforcement learning approach, and propose an enhanced method for learning the Q-function for actors within the MA-DDPG framework. Using the Mojiko Fireworks Festival dataset as a case study, we evaluated the effectiveness of our proposed method by comparing congestion levels with existing approaches. The results demonstrate that our method successfully reduces congestion, with agents exhibiting superior cooperation in managing crowd flow. This improvement in agent coordination suggests the potential for practical applications in real-world crowd management scenarios.