A deep reinforcement learning method for multi-heterogeneous robot systems based on global status

Abstract

At present, multi-agent reinforcement learning (MARL) is in the initial stage of development, and research on strongly coupled multi-agent reinforcement learning is not in-depth. We propose a deep reinforcement learning network architecture for multi-heterogeneous robot systems based on a global status simulation platform. This architecture is based on clipped PPO and constructs a strategy model by constructing the full state action space and full action space of multi-heterogeneous robots. We trained and tested the algorithm on a multi-heterogeneous robot simulation platform, and the results showed that the proposed method can effectively target artificially fixed strategies and achieve victory.