Enhanced Causal Reasoning and Graph Networks for Multi-agent Path Finding

Multi-Agent Path Finding refers to the problem of finding the optimal path set for multiple agents from the starting position to the target position without conflict. In multi-agent path planning, multiple factors need to be considered, such as the dynamic characteristics of the agent, environmental constraints, communication and collaboration mechanisms, etc. In order to solve such problems, researchers have proposed a variety of path planning algorithms, including centralized path planning algorithms, distributed path planning algorithms and collaborative path planning algorithms. In this paper, we propose a decentralized multi-agent path planning framework that combines causal relationship drive and graph neural network(CRGN). First, we propose an end-to-end framework to extract global features and local features in images. On this basis, we design the fusion of deep features and shallow features to increase visual fine-graining. Then, we propose to use graph neural networks to aggregate information communication between agents and build a dynamic communication topology network through agent communication within the local field of view. Finally, we propose a new reinforcement learning algorithm to train the agent’s generalization ability in unknown environments. We fit the state value function and action value function in reinforcement learning through deep neural networks. Thus, it can better solve the intelligent agent path planning under large-scale complex problems. We verified the effectiveness of the model on multiple different datasets. Experimental results show that our proposed approach is close to the performance of expert algorithms. At the same time, we show through extensive ablation experiments that our model can exhibit good performance and robustness even in unseen scenes.