Learning classifier systems (LCSs) are population-based predictive systems
that were originally envisioned as agents to act in reinforcement learning (RL)
environments. These systems can suffer from population bloat and so are
amenable to compaction techniques that try to strike a balance between
population size and performance. A well-studied LCS architecture is XCSF, which
in the RL setting acts as a Q-function approximator. We apply XCSF to a
deterministic and stochastic variant of the FrozenLake8x8 environment from
OpenAI Gym, with its performance compared in terms of function approximation
error and policy accuracy to the optimal Q-functions and policies produced by
solving the environments via dynamic programming. We then introduce a novel
compaction algorithm (Greedy Niche Mass Compaction - GNMC) and study its
operation on XCSF's trained populations. Results show that given a suitable
parametrisation, GNMC preserves or even slightly improves function
approximation error while yielding a significant reduction in population size.
Reasonable preservation of policy accuracy also occurs, and we link this metric
to the commonly used steps-to-goal metric in maze-like environments,
illustrating how the metrics are complementary rather than competitive