【摘要】 多智能体路径规划算法已经广泛应用于现实问题。基于强化学习的方法通常将其建模为部分可观察的马尔可夫决策过程，其中智能体根据局部观察独立做出决策。引入通信可以解决局部观察信息不足的问题，然而现有的通信方案常常忽略智能体之间过量的信息交换，导致额外的系统开销和延迟。为提升信息交换效率，提出了一种基于通信的多智能体路径规划算法，应用随机通信丢弃技术来解决由于智能体过度依赖通信而导致的系统不稳定问题。实验结果表明，引入随机通信丢弃可以更有效地协调智能体的行为。更多还原

【Abstract】 Multi-agent pathfinding algorithm has been widely applied to practical problems. Reinforcement learning based approaches typically model multi-agent pathfinding as a partially observable Markov decision process in which agents make decisions independently based on local observations. The introduction of communication solves the problem of insufficient local observation information, but the existing communication schemes often ignore excessive information exchange between agents, resulting in additional system overhead and latency. In order to improve the efficiency of information exchange, a communication based multi-agent path planning algorithm is proposed. Random communication discarding technology is applied to solve the problem of system instability caused by excessive dependence on communication. Random communication dropout technique is applied to solve the problem of system instability caused by excessive dependence on communication. Experimental results show that introducing random communication dropout can coordinate the behavior of agents more effectively.更多还原