  Model-free reinforcement learning (RL) is a powerful tool to learn a broad
range of robot skills and policies. However, a lack of policy interpretability
can inhibit their successful deployment in downstream applications,
particularly when differences in environmental conditions may result in
unpredictable behaviour or generalisation failures. As a result, there has been
a growing emphasis in machine learning around the inclusion of stronger
inductive biases in models to improve generalisation. This paper proposes an
alternative strategy, inverse value estimation for interpretable policy
certificates (IV-Posterior), which seeks to identify the inductive biases or
idealised conditions of operation already held by pre-trained policies, and
then use this information to guide their deployment. IV-Posterior uses
MaskedAutoregressive Flows to fit distributions over the set of conditions or
environmental parameters in which a policy is likely to be effective. This
distribution can then be used as a policy certificate in downstream
applications. We illustrate the use of IV-Posterior across a two environments,
and show that substantial performance gains can be obtained when policy
selection incorporates knowledge of the inductive biases that these policies
hold.
