System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning

Understand

Evolutionary science provides evidence that diversity confers resilience in natural systems.

• Yet, traditional multi-agent reinforcement learning techniques commonly enforce homogeneity to increase training sample efficiency.
• When a system of learning agents is not constrained to homogeneous policies, individuals may develop diverse behaviors, resulting in emergent complementarity that benefits the system.
• Despite this, there is a surprising lack of tools that quantify behavioral diversity.