Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control
This addresses the sample inefficiency issue in multi-agent systems like UAVs and AUVs, offering a practical improvement for flocking control applications.
The paper tackles the sample inefficiency problem in multi-agent reinforcement learning for flocking control by proposing PwD-MARL, a method that pretrains agents using non-expert demonstrations, resulting in improved sample efficiency and policy performance, even with poor or limited demonstrations.
Flocking control is a significant problem in multi-agent systems such as multi-agent unmanned aerial vehicles and multi-agent autonomous underwater vehicles, which enhances the cooperativity and safety of agents. In contrast to traditional methods, multi-agent reinforcement learning (MARL) solves the problem of flocking control more flexibly. However, methods based on MARL suffer from sample inefficiency, since they require a huge number of experiences to be collected from interactions between agents and the environment. We propose a novel method Pretraining with Demonstrations for MARL (PwD-MARL), which can utilize non-expert demonstrations collected in advance with traditional methods to pretrain agents. During the process of pretraining, agents learn policies from demonstrations by MARL and behavior cloning simultaneously, and are prevented from overfitting demonstrations. By pretraining with non-expert demonstrations, PwD-MARL improves sample efficiency in the process of online MARL with a warm start. Experiments show that PwD-MARL improves sample efficiency and policy performance in the problem of flocking control, even with bad or few demonstrations.