Explaining Reinforcement Learning

 Been recently involved with reinforcement learning, and found this interesting.   I see some advances in this space worth following.  Especially in RL, you need explanation regarding decisions, in order to support reasonable tuning.  

Dear Reinforcement Learning Agent, please explain your actions.

Explainable Reinforcement Learning for Longitudinal Control

Roman Liessner   in TowardDataScience

Here I present research with Jan Dohmen and Marco Wiering.

TL;DR: Reinforcement learning is promising for achieving new best performances in a variety of applications. However, as long as the learned actions remain intransparent, their use in security-relevant applications is unlikely. The new RL-SHAP Diagram presented here opens the black box and gives a new perspective to the Reinforcement Learning decision-making. ..."