Deep Reinforcement Learning with a Classifier System - First Steps

Organic Computing enables self-* properties in technical systems for mastering them in the face of complexity and for improving robustness and efficiency. Key technology for self-improving adaptation decisions is reinforcement learning (RL). In this paper, we argue that traditional deep RL concepts are not applicable due to their limited interpretability. In contrast, approaches from the field of rule-based evolutionary RL are less powerful. We propose to fuse both technical concepts while maintaining their advantages - allowing for an applicability especially suited for Organic Computing applications. We present initial steps and the first evaluation of standard RL scenarios.