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Entropy-based local fitnesses for evolutionary multiagent systems

Evolutionary multiagent systems have been successfully applied to many real world problems, including search and rescue and ocean exploration. However, as the number of agents increases in such problems, the evaluation function captures an individual agent's fitness less and less accurately. As a consequence, agents adopt a small set of acceptable behaviors that are neither optimal nor robust to environmental changes or teammate failures. Fitness shaping, intrinsic fitnesses, or multi-fitness learning alleviate some of these concerns but generally require domain knowledge or the functional form of the evaluation function. In this paper, we introduce Entropy-Based Local Fitnesses (EBLFs) that generate diverse behaviors for agents and produce robust team behaviors without requiring environmental knowledge. The key contribution of EBLFs is to inject a dense, entropy-based fitness into the agents' evolution without interfering with the sparse, high-level system evaluation function. Our results show that the agents using EBLFs learn new skills in difficult environments with sparse feedback without requiring domain knowledge. In addition, EBLFs generated new team-level behaviors that were not defined by a human operator, but beneficial to robust team performance.