  In model-based reinforcement learning it is typical to decouple the problems
of learning the dynamics model and learning the reward function. However, when
the dynamics model is flawed, it may generate erroneous states that would never
occur in the true environment. It is not clear a priori what value the reward
function should assign to such states. This paper presents a novel error bound
that accounts for the reward model's behavior in states sampled from the model.
This bound is used to extend the existing Hallucinated DAgger-MC algorithm,
which offers theoretical performance guarantees in deterministic MDPs that do
not assume a perfect model can be learned. Empirically, this approach to reward
learning can yield dramatic improvements in control performance when the
dynamics model is flawed.
