Multi-Agent Reinforcement Learning with Intrinsic Motivation and Cooperative Reward Shaping

Abstract

Multi-Agent Reinforcement Learning (MARL) offers a promising approach to tackling complex cooperative tasks. However, existing MARL algorithms often fail to achieve robust coordination and cooperation, primarily due to difficulties in learning effective joint policies and the lack of mechanisms to encourage collaborative behavior. This paper proposes a novel framework that integrates intrinsic motivation and cooperative reward shaping into a MARL system. The core idea is to augment the traditional extrinsic reward signal with internal drives, such as curiosity and competence, and to shape the reward function to explicitly incentivize cooperation among agents. We introduce a framework where agents learn to maximize both their external rewards and their internal motivation levels, while simultaneously benefiting from a carefully designed cooperative reward structure. The theoretical analysis demonstrates the potential of this approach to overcome the limitations of standard MARL and to promote more efficient and stable cooperative learning. We present a detailed description of the framework and discuss its key components, highlighting the interplay between intrinsic motivation, cooperative reward shaping, and the overall learning process. The results, although presented without empirical experimentation, illustrate the potential impact of this approach on improving cooperative MARL performance. ---