A Novel Multi-Agent Parallel-Critic Network Architecture for Cooperative-Competitive Reinforcement Learning

Multi-agent deep reinforcement learning (MDRL) is an emerging research hotspot and application direction in the field of machine learning and artificial intelligence. MDRL covers many algorithms, rules and frameworks, it is currently researched in swarm system, energy allocation optimization, stocking analysis, sequential social dilemma, and with extremely bright future. In this paper, a parallel-critic method based on classic MDRL algorithm MADDPG is proposed to alleviate the training instability problem in cooperative-competitive multi-agent environment. Furthermore, a policy smoothing technique is introduced to our proposed method to decrease the variance of learning policies. The suggested method is evaluated in three different scenarios of authoritative multi-agent particle environment (MPE). Multiple statistical data of experimental results show that our method significantly improves the training stability and performance compared to vanilla MADDPG.