Deep Reinforcement Learning (DRL) and Evolution Strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist. To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects such as scalability, exploration, adaptation to dynamic environments, and multi-agent learning. Then, the benefits of hybrid algorithms that combine concepts from DRL and ESs are highlighted. Finally, to have an indication about how they compare in real-world applications, a survey of the literature for the set of applications they support is provided.
翻译:深入强化学习(DRL)和进化战略(ES)在许多相继决策问题上超越了人的水平控制,但仍然存在许多公开的挑战。为了深入了解DRL相对于ES的长处和短处,提供了对其各自能力和局限性的分析。在介绍其基本概念和算法之后,对可扩展性、探索、适应动态环境和多试剂学习等关键方面进行了比较。然后,强调了将DRL和ES的概念结合起来的混合算法的好处。最后,为了说明它们如何在现实世界应用中进行比较,提供了对其所支持的一系列应用的文献调查。