Dynamic Algorithm Configuration

Funding Agency

We will model the algorithm control as a reinforcement learning (RL) problem, which means that the actions change parameter configurations, and the states correspond to the states of an algorithm solving a problem instance. Deep RL recently showed that challenging problems can be learned, e.g., the game of Go, playing Atari games or Poker. We believe that the recent progress in deep RL will enable us to successfully obtain effective control policies and we dub our approach deep algorithm control (DAC). DAC is similar to these game applications and therefore a promising research direction: (i) we can collect large amounts of training data in an offline phase by evaluating different policies on sets of instances, (ii) the state and action space is in both applications huge, and (iii) deep learning models can predict the performance of AI algorithms.

By successfully obtaining effective DAC policies, this approach will be a powerful generalization of other meta-algorithmic approaches, such as algorithm configuration, algorithm selection and their combinations. Thus we believe that DAC is a promising research direction that will have a huge impact on many AI fields such as algorithm configuration and algorithm selection previously had.

Lead at LUHAI: Prof. Lindauer

Funding Program:  DFG

Project Period: 2020-2024