使用深度网格在噪声领域中实现快速稳定的 MAP-Elites
Fast and stable MAP-Elites in noisy domains using deep grids
AI总结：
针对 QD 算法在适应度与行为描述符噪声下评估成本高的问题，提出利用相似历史方案档案估计性能的 Deep-Grid MAP-Elites，并在三类噪声任务中证明其更稳健、样本效率更高。
摘要：
质量-多样性优化算法能够演化出兼具高性能与多样性的解决方案集合。这些集合使得在某个方案未按预期工作时，可以快速适应并从一个方案切换到另一个方案。因此，它在机器人控制等现实领域问题中得到了许多应用。然而，与大多数优化算法一样，QD 算法不仅对适应度函数上的不确定性非常敏感，对行为描述符上的不确定性也非常敏感。然而，此类不确定性在现实应用中十分常见。很少有研究在 QD 算法这一特定情形下探讨该问题；受进化计算文献启发，已有研究主要关注使用采样来近似解决方案性能的“真实”值。但是，采样方法需要大量评估，在机器人等许多应用中，这很快会变得不切实际。在本研究中，我们提出 Deep-Grid MAP-Elites，这是 MAP-Elites 算法的一个变体，它使用先前遇到的相似解决方案档案来近似某个解决方案的性能。我们在三个噪声任务上将我们的方法与此前探讨过的方法进行比较：一个标准优化任务、一个冗余机械臂控制任务，以及一个模拟六足机器人任务。实验结果表明，这一简单方法对行为描述符上的噪声具有显著更强的韧性，同时在适应度优化方面取得具有竞争力的性能，并且比其他现有方法具有更高的样本效率。
英文摘要：
Quality-Diversity optimisation algorithms enable the evolution of collections of both high-performing and diverse solutions. These collections offer the possibility to quickly adapt and switch from one solution to another in case it is not working as expected. It therefore finds many applications in real-world domain problems such as robotic control. However, QD algorithms, like most optimisation algorithms, are very sensitive to uncertainty on the fitness function, but also on the behavioural descriptors. Yet, such uncertainties are frequent in real-world applications. Few works have explored this issue in the specific case of QD algorithms, and inspired by the literature in Evolutionary Computation, mainly focus on using sampling to approximate the "true" value of the performances of a solution. However, sampling approaches require a high number of evaluations, which in many applications such as robotics, can quickly become impractical. In this work, we propose Deep-Grid MAP-Elites, a variant of the MAP-Elites algorithm that uses an archive of similar previously encountered solutions to approximate the performance of a solution. We compare our approach to previously explored ones on three noisy tasks: a standard optimisation task, the control of a redundant arm and a simulated Hexapod robot. The experimental results show that this simple approach is significantly more resilient to noise on the behavioural descriptors, while achieving competitive performances in terms of fitness optimisation, and being more sample-efficient than other existing approaches.
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