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Shannon's information entropy measures of the uncertainty of an event'soutcome. If learning about a system reflects a decrease in uncertainty, then aplausible intuition is that learning should be accompanied by a decrease in theentropy of the organism's actions and/or perceptual states. To address whetherthis intuition is valid, I examined an artificial organism -- a simple robot --that learned to navigate in an arena and analyzed the entropy of the outcomevariables action, state, and reward. Entropy did indeed decrease in the initialstages of learning, but two factors complicated the scenario: (1) theintroduction of new options discovered during the learning process and (2) theshifting patterns of perceptual and environmental states resulting from changesto the robot's learned movement strategies. These factors lead to a subsequentincrease in entropy as the agent learned. I end with a discussion of theutility of information-based characterizations of learning.

Does Learning Imply a Decrease in the Entropy of Behavior?