Associative Reinforcement Learning using Linear Probabilistic Concepts

Abstract

We consider the problem of maximizing the total number of successes while learning about a probability function determining the likelihood of a success. In particular, we consider the case in which the probability function is represented by a linear function of the attribute vector associated with each action/choice. In the scenario we consider, learning proceeds in trials and in each trial, the algorithm is given a number of alternatives to choose from, each having an attribute vector associated with it, and for the alternative it selects it gets either a success or a failure with probability determined by applying a fixed but unknown linear success probability function to the attribute vector. Our algorithms consist of a learning method like the Widrow-Hoff rule and a probabilistic selection strategy which work together to resolve the so-called exploration-exploitation tradeoff. We analyze the performance of these methods by proving bounds on the worst-case regret, or how many less ...

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