论文标题
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Fast and stable MAP-Elites in noisy domains using deep grids
论文作者
论文摘要
质量多样性优化算法使高性能和多样的解决方案的收集能够演变。这些收藏品提供了可以快速调整并从一种解决方案转换为另一种解决方案的可能性，以防其正常工作。因此，它发现了许多应用程序中的许多应用程序，例如机器人控制。但是，与大多数优化算法一样，QD算法对适应性函数的不确定性非常敏感，并且对行为描述符也非常敏感。但是，在现实世界中，这种不确定性经常存在。在QD算法的特定情况下，很少有作品探讨了此问题，并且受到进化计算中文献的启发，主要集中于使用采样来近似解决方案性能的“真实”值。但是，采样方法需要大量的评估，在许多应用程序（例如机器人）中，这些评估可能很快变得不切实际。在这项工作中，我们提出了Deep-Grid Map-Elites，这是MAP-ELITE算法的变体，该算法使用类似的先前遇到的解决方案的存档来近似于解决方案的性能。我们比较了以前在三个嘈杂任务上探讨的方法：标准优化任务，冗余臂的控制和模拟的六型六型机器人。实验结果表明，这种简单的方法对行为描述符上的噪声更有弹性，同时在健身优化方面实现了竞争性能，并且比其他现有方法更有效率。
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.
