Debugging a Policy: A Framework for Automatic Action Policy Testing

Neural network (NN) action policies are an attractive option for real-time action decisions in dynamic environments. Yet this requires a high degree of trust in the NN. How to gain such trust? Systematic testing certainly is one possible answer, in analogy to program testing. The input to the program becomes the start state for the policy; and erroneous program behaviors – “bugs” – become bad policy behavior, e. g. not reaching the goal from a solvable state. We introduce a framework spelling out this concept. The framework is generic and in principle applicable to arbitrary planning models. We discuss how this form of testing can be operationalized, i. e. , how to confirm a bug has been found, and how potential bugs might be identified in the first place. This essentially involves seeing standard planning concepts through the new lense of policy testing. The implementation and practical exploration of this framework remains open for future work. We believe that action policy testing is an important topic for ICAPS, and we hope that our framework will serve to start its discussion.

Authors

• Marcel Steinmetz
• Timo P. Gros
• Philippe Heim
• Daniel Höller
• Joerg Hoffmann

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