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Reinforcement learning as classification: leveraging modern classifiers

Lagoudakis Michael, Parr, R.

Πλήρης Εγγραφή

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Περίληψη

The basic tools of machine learning appear inthe inner loop of most reinforcement learning algorithms,typically in the form of Monte Carlomethods or function approximation techniques.To a large extent, however, current reinforcementlearning algorithms draw upon machine learningtechniques that are at least ten years old and,with a few exceptions, very little has been doneto exploit recent advances in classification learningfor the purposes of reinforcement learning.We use a variant of approximate policy iterationbased on rollouts that allows us to use a pure classificationlearner, such as a support vector machine(SVM), in the inner loop of the algorithm.We argue that the use of SVMs, particularly incombination with the kernel trick, can make iteasier to apply reinforcement learning as an “outof-the-box”technique, without extensive featureengineering. Our approach opens the door tomodern classification methods, but does not precludethe use of classical methods. We presentexperimental results in the pendulum balancingand bicycle riding domains using both SVMs andneural networks for classifiers

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