基于模仿学习的替代式多智能体近端策略优化算法用于规整蜂群式追逃规避
Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance
AI总结：
针对去中心化大规模多机器人系统追逃规避任务的复合能力需求，提出基于模仿学习的替代式多智能体近端策略优化算法，在大幅降低通信开销的同时实现接近集中式方案的性能。
摘要：
多机器人系统（Multi-Robot System, MRS）已引发广泛研究兴趣，并催生了大量引人关注的应用，尤其在协同控制领域。然而，针对去中心化大规模MRS在追逃规避任务中的编队、监视与防御复合能力仍鲜有研究，这对协同能力与适应性提出了严苛要求。本文提出一种去中心化的基于模仿学习的替代式多智能体近端策略优化（Imitation learning based Alternative Multi-Agent Proximal Policy Optimization, IA-MAPPO）算法，为规整蜂群执行追逃规避任务提供灵活且通信开销低的解决方案。具体而言，首先设计了基于策略蒸馏的MAPPO执行器，能够以集中式方式完成多种编队并在其间快速切换。此外，我们利用模仿学习对编队控制器进行去中心化处理，以降低通信开销并提升可扩展性。随后，采用替代式训练来弥补去中心化带来的性能损失。仿真结果验证了IA-MAPPO的有效性，大量消融实验进一步表明，其性能与集中式解决方案相当，且通信开销显著降低。
英文摘要：
Multi-Robot System (MRS) has garnered widespread research interest and fostered tremendous interesting applications, especially in cooperative control fields. Yet little light has been shed on the compound ability of formation, monitoring and defence in decentralized large-scale MRS for pursuit avoidance, which puts stringent requirements on the capability of coordination and adaptability. In this paper, we put forward a decentralized Imitation learning based Alternative Multi-Agent Proximal Policy Optimization (IA-MAPPO) algorithm to provide a flexible and communication-economic solution to execute the pursuit avoidance task in well-formed swarm. In particular, a policy-distillation based MAPPO executor is firstly devised to capably accomplish and swiftly switch between multiple formations in a centralized manner. Furthermore, we utilize imitation learning to decentralize the formation controller, so as to reduce the communication overheads and enhance the scalability. Afterwards, alternative training is leveraged to compensate the performance loss incurred by decentralization. The simulation results validate the effectiveness of IA-MAPPO and extensive ablation experiments further show the performance comparable to a centralized solution with significant decrease in communication overheads.
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