  For reinforcement learning systems to be widely adopted, their users must
understand and trust them. We present a theoretical analysis of explaining
reinforcement learning using Shapley values, following a principled approach
from game theory for identifying the contribution of individual players to the
outcome of a cooperative game. We call this general framework Shapley Values
for Explaining Reinforcement Learning (SVERL). Our analysis exposes the
limitations of earlier uses of Shapley values in reinforcement learning. We
then develop an approach that uses Shapley values to explain agent performance.
In a variety of domains, SVERL produces meaningful explanations that match and
supplement human intuition.

Machine Learning