  Optimal control problems with free terminal time present many challenges
including nonsmooth and discontinuous control laws, irregular value functions,
many local optima, and the curse of dimensionality. To overcome these issues,
we propose an adaptation of the model-based actor-critic paradigm from the
field of Reinforcement Learning via an exponential transformation to learn an
approximate feedback control and value function pair. We demonstrate the
algorithm's effectiveness on prototypical examples featuring each of the main
pathological issues present in problems of this type.
