Abstract
Multi-Agent Reinforcement Learning (MARL) provides an effective paradigm for distributed Multi-Agent Path Finding (MAPF) under partial observability. Due to frequent local interactions among agents, action decisions based on limited local information can induce potential collision risks and may further lead to action conflicts, posing challenges to efficient multi-agent coordination. In this paper, we propose CPH, a value-based MARL framework that proactively mitigates collision risks during path decision-making and resolves unavoidable action conflicts before execution. Specifically, we introduce a collision-risk-driven communication mechanism that selects partners according to potential collision relationships and integrates collision-aware spatial contexts with agent-centric observations via cross-attention. Moreover, we propose a potential-inspired aggregation reward shaping method, which encourages agents to disperse from collision-prone aggregation states. Additionally, we present a conflict-aware hybrid post-processing strategy that adaptively resolves different conflict scenarios through local action adjustment. Experimental results show that CPH outperforms the compared distributed MARL-based MAPF methods, while ablation studies confirm the effectiveness of all three components. These results indicate that combining proactive collision-risk mitigation with adaptive conflict resolution before execution improves path reachability and planning efficiency in distributed MAPF.