  Interest in reinforcement learning (RL) has recently surged due to the
application of deep learning techniques, but these connectionist approaches are
opaque compared with symbolic systems. Learning Classifier Systems (LCSs) are
evolutionary machine learning systems that can be categorised as eXplainable AI
(XAI) due to their rule-based nature. Michigan LCSs are commonly used in RL
domains as the alternative Pittsburgh systems (e.g. SAMUEL) suffer from complex
algorithmic design and high computational requirements; however they can
produce more compact/interpretable solutions than Michigan systems. We aim to
develop two novel Pittsburgh LCSs to address RL domains: PPL-DL and PPL-ST. The
former acts as a "zeroth-level" system, and the latter revisits SAMUEL's core
Monte Carlo learning mechanism for estimating rule strength. We compare our two
Pittsburgh systems to the Michigan system XCS across deterministic and
stochastic FrozenLake environments. Results show that PPL-ST performs on-par or
better than PPL-DL and outperforms XCS in the presence of high levels of
environmental uncertainty. Rulesets evolved by PPL-ST can achieve higher
performance than those evolved by XCS, but in a more parsimonious and therefore
more interpretable fashion, albeit with higher computational cost. This
indicates that PPL-ST is an LCS well-suited to producing explainable policies
in RL domains.
