A high-performance ACS2 based on vectorization and GPU

Abstract

Anticipatory Learning Classifier Systems (ALCS) provide an interpretable symbolic approach to reinforcement learning, but their practical use is often limited by the cost of maintaining evolving rule populations. This paper presents a unified ACS2 framework for comparing five execution regimes: a symbolic baseline, single-process CPU execution, multiprocessing CPU execution, sequential independently seeded tensorized GPU execution, and the same PyTorch tensor backend executed on the host CPU. The contribution is primarily systems- and methodology-oriented rather than algorithmic: the framework enables controlled comparison of runtime, exploit-phase behavior, and maintained population size across distinct ACS2 implementations. The tensorized backend reformulates nearly the full ACS2 learning loop in dense tensor form, including matching, anticipatory updates, offspring handling, and fixed-capacity population management. Benchmarking over the full ACS2 maze suite shows that the multiprocessing CPU backend provides the best overall wall-clock runtime, whereas the single-process CPU backend yields the best average per-experiment runtime. The tensorized modes achieve lower exploit-step averages, but they remain substantially more expensive in runtime terms and must be interpreted cautiously because backend realization also changes population-management semantics. The resulting framework therefore contributes a reproducible basis for studying runtime-population trade-offs and semantic divergence across ACS2 execution strategies.