  Sample efficiency is a critical property when optimizing policy parameters
for the controller of a robot. In this paper, we evaluate two state-of-the-art
policy optimization algorithms. One is a recent deep reinforcement learning
method based on an actor-critic algorithm, Deep Deterministic Policy Gradient
(DDPG), that has been shown to perform well on various control benchmarks. The
other one is a direct policy search method, Covariance Matrix Adaptation
Evolution Strategy (CMA-ES), a black-box optimization method that is widely
used for robot learning. The algorithms are evaluated on a continuous version
of the mountain car benchmark problem, so as to compare their sample
complexity. From a preliminary analysis, we expect DDPG to be more sample
efficient than CMA-ES, which is confirmed by our experimental results.

Machine Learning