Metamorphic relations via relaxations: an approach to obtain oracles for action-policy testing

Abstract

Testing is a promising way to gain trust in a learned action policy π, in particular if π is a neural network. A “bug” in this context constitutes undesirable or fatal policy behavior, e.g., satisfying a failure condition. But how do we distinguish whether such behavior is due to bad policy decisions, or whether it is actually unavoidable under the given circumstances? This requires knowledge about optimal solutions, which defeats the scalability of testing. Related problems occur in software testing when the correct program output is not known.