Parallel Random Embedding with Negatively Correlated Search

Evolutionary algorithm (EA) is proved to be a promising way for parameter optimization in deep reinforcement learning (RL) in recent years. However, it still suffers from the curse of dimensionality when dealing with high-dimensional inputs. Based on experiments, we observe that only a few variables contribute significantly to the performance of large-scale RL policy. Intuitively, we propose a parallel random embedding framework to optimize strategies on multiple parameter sub-spaces to incorporate classical Evolution algorithms and techniques for the million-scale RL policy optimization. Experiments show that our approach has outperforming performance with Negatively Correlation Search (NCS) in the framework.