On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL
CoRR(2023)
Abstract
Cooperative multi-agent reinforcement learning (MARL) has made substantial
strides in addressing the distributed decision-making challenges. However, as
multi-agent systems grow in complexity, gaining a comprehensive understanding
of their behaviour becomes increasingly challenging. Conventionally, tracking
team rewards over time has served as a pragmatic measure to gauge the
effectiveness of agents in learning optimal policies. Nevertheless, we argue
that relying solely on the empirical returns may obscure crucial insights into
agent behaviour. In this paper, we explore the application of explainable AI
(XAI) tools to gain profound insights into agent behaviour. We employ these
diagnostics tools within the context of Level-Based Foraging and Multi-Robot
Warehouse environments and apply them to a diverse array of MARL algorithms. We
demonstrate how our diagnostics can enhance the interpretability and
explainability of MARL systems, providing a better understanding of agent
behaviour.
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