论文标题
多任务优化的质量多样性
Quality Diversity for Multi-task Optimization
论文作者
论文摘要
质量多样性（QD）算法是最新的优化算法系列，它们搜索了一系列多样化但高性能的解决方案。在某些特定情况下，他们可以一次解决多个任务。例如，他们可以找到到达一组点所需的关节位置，这也可以通过为每个目标点运行经典优化器来解决。但是，当需要独立评估每个任务的健身时（例如，优化策略以掌握许多不同的对象）时，它们无法解决多个任务。在本文中，我们提出了一个称为多任务MAP-ELITE的MAP-ELITE算法的扩展，该算法在健身函数取决于任务时求解了多个任务。我们通过模拟的参数化平面臂（10维搜索空间； 5000个任务）和具有不同长度的腿（36维搜索空间； 2000年任务）进行评估。结果表明，在这两种情况下，我们的算法都用CMA-ES算法分别优于每个任务的优化。
Quality Diversity (QD) algorithms are a recent family of optimization algorithms that search for a large set of diverse but high-performing solutions. In some specific situations, they can solve multiple tasks at once. For instance, they can find the joint positions required for a robotic arm to reach a set of points, which can also be solved by running a classic optimizer for each target point. However, they cannot solve multiple tasks when the fitness needs to be evaluated independently for each task (e.g., optimizing policies to grasp many different objects). In this paper, we propose an extension of the MAP-Elites algorithm, called Multi-task MAP-Elites, that solves multiple tasks when the fitness function depends on the task. We evaluate it on a simulated parameterized planar arm (10-dimensional search space; 5000 tasks) and on a simulated 6-legged robot with legs of different lengths (36-dimensional search space; 2000 tasks). The results show that in both cases our algorithm outperforms the optimization of each task separately with the CMA-ES algorithm.
