  Parameter space exploration methods with black-box optimization have recently
been shown to outperform state-of-the-art approaches in continuous control
reinforcement learning domains. In this paper, we examine reasons why these
methods work better and the situations in which they are worse than traditional
action space exploration methods. Through a simple theoretical analysis, we
show that when the parametric complexity required to solve the reinforcement
learning problem is greater than the product of action space dimensionality and
horizon length, exploration in action space is preferred. This is also shown
empirically by comparing simple exploration methods on several toy problems.
