  Evolutionary algorithms (EAs) have been successfully applied to optimize the
policies for Reinforcement Learning (RL) tasks due to their exploration
ability. The recently proposed Negatively Correlated Search (NCS) provides a
distinct parallel exploration search behavior and is expected to facilitate RL
more effectively. Considering that the commonly adopted neural policies usually
involves millions of parameters to be optimized, the direct application of NCS
to RL may face a great challenge of the large-scale search space. To address
this issue, this paper presents an NCS-friendly Cooperative Coevolution (CC)
framework to scale-up NCS while largely preserving its parallel exploration
search behavior. The issue of traditional CC that can deteriorate NCS is also
discussed. Empirical studies on 10 popular Atari games show that the proposed
method can significantly outperform three state-of-the-art deep RL methods with
50% less computational time by effectively exploring a 1.7 million-dimensional
search space.
