  Lengthy evaluation times are common in many optimization problems such as
direct policy search tasks, especially when they involve conducting evaluations
in the physical world, e.g. in robotics applications. Often when evaluating
solution over a fixed time period it becomes clear that the objective value
will not increase with additional computation time (for example when a two
wheeled robot continuously spins on the spot). In such cases, it makes sense to
stop the evaluation early to save computation time. However, most approaches to
stop the evaluation are problem specific and need to be specifically designed
for the task at hand. Therefore, we propose an early stopping method for direct
policy search. The proposed method only looks at the objective value at each
time step and requires no problem specific knowledge. We test the introduced
stopping criterion in five direct policy search environments drawn from games,
robotics and classic control domains, and show that it can save up to 75% of
the computation time. We also compare it with problem specific stopping
criteria and show that it performs comparably, while being more generally
applicable.
