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In the age of advanced technology and automation, self-organizing systems capable of adapting flexibly to diverse situations are becoming increasingly critical. Organic Computing (OC) addresses this challenge by drawing inspiration from biological processes to develop systems that respond autonomously and robustly to environmental changes. Within such systems, decision-making plays a pivotal role, with the XCS classifier system (XCS) emerging as a prominent method. Another category of algorithms that fits this requirement is reinforcement learning (RL). However, a comprehensive comparison of XCS and RL remains scarce. This paper bridges this gap by presenting an extensive empirical analysis of XCS and various RL algorithms across 51 problem instances from five established RL benchmarks of varying complexity. The results show that while XCS performs competitively on simpler problems, it fails completely on two of the benchmarks, making RL a more suitable alternative to XCS in selected OC systems. While XCS is robust in noisy environments, this trait is not unique. Other algorithms exhibit comparable stability and significantly outperform XCS in complex scenarios with larger state and action spaces.

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Evaluating Adaptive Systems: A Comparative Study of XCS and Established Reinforcement Learning Algorithms in Noisy Multi-step Environments

  • 会议论文

  • 发表日期：

    2026年

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  • Marco Steinberger,
  • Roman Küble,
  • Michael Heider,
  • Jörg Hähner

摘要

In the age of advanced technology and automation, self-organizing systems capable of adapting flexibly to diverse situations are becoming increasingly critical. Organic Computing (OC) addresses this challenge by drawing inspiration from biological processes to develop systems that respond autonomously and robustly to environmental changes. Within such systems, decision-making plays a pivotal role, with the XCS classifier system (XCS) emerging as a prominent method. Another category of algorithms that fits this requirement is reinforcement learning (RL). However, a comprehensive comparison of XCS and RL remains scarce. This paper bridges this gap by presenting an extensive empirical analysis of XCS and various RL algorithms across 51 problem instances from five established RL benchmarks of varying complexity. The results show that while XCS performs competitively on simpler problems, it fails completely on two of the benchmarks, making RL a more suitable alternative to XCS in selected OC systems. While XCS is robust in noisy environments, this trait is not unique. Other algorithms exhibit comparable stability and significantly outperform XCS in complex scenarios with larger state and action spaces.