Learning adversarial policy in multiple scenes environment via multi-agent reinforcement learning

Abstract

ABSTRACT Learning adversarial policy aims to learn behavioural strategies for agents with different goals, is one of the most significant tasks in multi-agent systems. Multi-agent reinforcement learning (MARL), as a state-of-the-art learning-based model, employs centralised or decentralised control methods to learn behavioural strategies by interacting with environments. It suffers from instability and slowness in the training process. Considering that parallel simulation or computation is an effective way to improve training performance, we propose a novel MARL method called Multiple scenes multi-agent proximal Policy Optimisation (MPO) in this paper. In MPO, we first simulate multiple parallel scenes in the training environment. Multiple policies control different agents in the same scene, and each policy also controls several identical agents from multiple scenes. Then, we expand proximal policy optimisation (PPO) with an improved actor-critic network, ensuring the stability of training in multi-agent tasks. The actor network only uses local information for decision making, and the critic network uses global information for training. Finally, effective training trajectories are computed with two criteria from multiple parallel scenes rather than single to accelerate the learning process. We evaluate our approach in two simulated 3D environments, one of which is Unity's official open-source soccer game, and the other is unmanned surface vehicles (USVs) built by Unity. Experiments demonstrate that MPO converges more stable and faster than benchmark methods in model training, and demonstrates excellent adversarial policy compared with benchmark models.