A Multiagent Cooperative Learning System With Evolution of Social Roles

Recent developments in reinforcement learning (RL) have been able to derive optimal policies for sophisticated and capable agents, and shown to achieve human-level performance on a number of challenging tasks. Unfortunately, when it comes to multiagent systems (MASs), complexities, such as nonstationarity and partial observability bring new challenges to the field. Building a flexible and efficient multiagent RL (MARL) algorithm capable of handling complex tasks has to date remained an open challenge. This article presents a multiagent learning system with the evolution of social roles (eSRMA). The main interest is placed on solving the key issues in the definition and evolution of suitable roles, and optimizing the policies accompanied by social roles in MAS efficiently. Specifically, eSRMA incorporates and cultivates role division awareness of agents to improve the ability to deal with complex cooperative tasks. Each agent is assigned a role module, which can dynamically generate roles based on the individuals' local observations. A novel MARL algorithm is designed as the principal driving force that governs the role-policy learning process by a role-attention credit assignment mechanism. Moreover, a role evolution process is developed to help agents dynamically choose appropriate roles in decision making. Comprehensive experiments on the StarCraft II micromanagement benchmarkhave demonstrated that eSRMA exhibits superiority in achieving higher learning capability and efficiency for multiple agents compared to the state-of-the-art MARL methods.