Explaining Sequences of Actions in Multi-agent Deep Reinforcement Learning Models

Khaing Phyo Wai, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Abstract: This paper introduces a method to explain MADRL agents' behaviors by abstracting their actions into high-level strategies. Particularly, a spatio-temporal neural network model is applied to encode the agents' sequences of actions as memory episodes wherein an aggregating memory retrieval can generalize them into a concise abstract representation of collective strategies. To assess the effectiveness of our method, we applied it to explain the actions of QMIX MADRL agents playing a StarCraft Multi-agent Challenge (SMAC) video game. A user study on the perceived explainability of the extracted strategies indicates that our method can provide comprehensible explanations at various levels of granularity.