OPTICAT: A Deep Reinforcement Learning Framework for Instance-Specific Algorithm Configuration

Abstract

Optimization algorithms contain parameters that greatly influence their behavior. Finding the right settings for parameters through automated algorithm configuration has become a critical component of designing competitive algorithms. While traditional offline configurators tackle this problem by finding one configuration that works well for a set of instances, instance-specific algorithm configuration utilizes features of the instances to provide configurations that are tailored to each instance to maximize performance. We propose the first instance-specific algorithm configurator based on deep reinforcement learning that can be used in general algorithm configuration settings. Our method is able to handle large, mixed, discrete and continuous search spaces and only requires a small number of instances for training. Not only does it select an individual configuration for every instance, it also selects configurations from a much broader range. We show that our configurator provides improvements over the state-of-the-art instance-specific configurators ISAC++ and Hydra on a wide range of problem domains.