We analyze the efficacy of modern neuro-evolutionary strategies for continuous control optimization. Overall the results collected on a wide variety of qualitatively different benchmark problems indicate that these methods are generally effective and scale well with respect to the number of parameters and the complexity of the problem. We demonstrate the importance of using suitable fitness functions or reward criteria since functions that are optimal for reinforcement learning algorithms tend to be sub-optimal for evolutionary strategies and vice versa. Finally, we provide an analysis of the role of hyper-parameters that demonstrates the importance of normalization techniques, especially in complex problems.
翻译:我们分析现代神经革命战略对持续控制优化的功效。总体而言,在各种性质不同的基准问题上收集的结果表明,这些方法在参数数量和问题的复杂性方面一般是有效的,规模也很大。我们证明使用适当的健身功能或奖励标准的重要性,因为对于加强学习算法最合适的功能往往对进化战略来说是次优的,反之亦然。最后,我们分析了显示正常化技术重要性的超参数的作用,特别是在复杂问题上。