Collective Intrinsic Motivation of a Multi-agent System Based on Reinforcement Learning Algorithms

One of the great challenges in reinforcement learning is learning an optimal behavior in environments with sparse rewards. Solving tasks in such setting require effective exploration methods that are often based on intrinsic rewards. Plenty of real-world problems involve sparse rewards and many of them are further complicated by multi-agent setting, where the majority of intrinsic motivation methods are ineffective. In this paper we address the problem of multi-agent environments with sparse rewards and propose to combine intrinsic rewards and multi-agent reinforcement learning (MARL) technics to create the Collective Intrinsic Motivation of Agents (CIMA) method. CIMA uses both the external reward and the intrinsic collective reward from the cooperative multi-agent system. The proposed method can be used along with any MARL method as base reinforcement learning algorithm. We compare CIMA with several state-of-the-art MARL methods within multi-agent environment with sparse rewards designed in StarCraft II.