ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. (arXiv:2211.03162v1 [cs.LG])

While deep reinforcement learning has proven to be successful in solving
control tasks, the "black-box" nature of an agent has received increasing
concerns. We propose a prototype-based post-hoc policy explainer, ProtoX, that
explains a blackbox agent by prototyping the agent's behaviors into scenarios,
each represented by a prototypical state. When learning prototypes, ProtoX
considers both visual similarity and scenario similarity. The latter is unique
to the reinforcement learning context, since it explains why the same action is
taken in visually …

arxiv prototyping reinforcement reinforcement learning