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Recent progress with BOXES

The BOXES algorithm of Michie and Chambers (1968) has proved to be an effective and flexible method for learning to control dynamic systems. The algorithm, in its original form has been used a benchmark for many experiments in control tasks such as pole balancing. Recent work in our laboratory has shown that the BOXES algorithm can be improved to yield very good learning rates. We describe experiments on a variety of update functions and discuss their robustness. We also develop the notion of freezing of BOXES, suggested by Michie and implemented by Bain (1990). We have also been concerned with synthesising a readable account of the control strategy employed by a set of boxes. Some preliminary work has begun in combining decision tree learning algorithms with BOXES. Using this method, we regard BOXES as the acquirer of sub-cognitive skills and the decision tree induction as a means of introspecting on the learned strategy to generate understandable control rules.