  Shannon's information entropy measures of the uncertainty of an event's
outcome. If learning about a system reflects a decrease in uncertainty, then a
plausible intuition is that learning should be accompanied by a decrease in the
entropy of the organism's actions and/or perceptual states. To address whether
this intuition is valid, I examined an artificial organism -- a simple robot --
that learned to navigate in an arena and analyzed the entropy of the outcome
variables action, state, and reward. Entropy did indeed decrease in the initial
stages of learning, but two factors complicated the scenario: (1) the
introduction of new options discovered during the learning process and (2) the
shifting patterns of perceptual and environmental states resulting from changes
to the robot's learned movement strategies. These factors lead to a subsequent
increase in entropy as the agent learned. I end with a discussion of the
utility of information-based characterizations of learning.
