  Modern learning classifier systems typically exploit a niched genetic
algorithm to facilitate rule discovery. When used for reinforcement learning,
such rules represent generalisations over the state-action-reward space. Whilst
encouraging maximal generality, the niching can potentially hinder the
formation of generalisations in the state space which are symmetrical, or very
similar, over different actions. This paper introduces the use of rules which
contain multiple actions, maintaining accuracy and reward metrics for each
action. It is shown that problem symmetries can be exploited, improving
performance, whilst not degrading performance when symmetries are reduced.
