Explanation of Reinforcement Learning Model in Dynamic Multi-Agent System

Understand

Recently, there has been increasing interest in transparency and interpretability in Deep Reinforcement Learning (DRL) systems.

• Verbal explanations, as the most natural way of communication in our daily life, deserve more attention, since they allow users to gain a better understanding of the system which ultimately could lead to a high level of trust and smooth collaboration.
• This paper reports a novel work in generating verbal explanations for DRL behaviors agent.
• A rule-based model is designed to construct explanations using a series of rules which are predefined with prior knowledge.