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DoubleQ-learning - NeurIPS
Double Q-learningHado van HasseltMulti-agent and Adaptive Computation GroupCentrum Wiskunde & InformaticaAbstractIn some stochastic environments the well-known reinforcement learning algo-rithm Q- learning performs very poorly. This poor performance is caused by largeoverestimations of action values. These overestimations result from a positivebias that is introduced because Q- learning uses the maximumaction value as anapproximation for the maximum expected action value. We introduce an alter-native way to approximate the maximum expected value for anyset of randomvariables.
1 Introduction Q-learning is a popular reinforcement learning algorithm that was proposed by Watkins [1] and can be used to optimally solve Markov Decision Processes (MDPs) [2]. We show that Q-learning’s performance can be poor in stochastic MDPs because of large overestimations of the action val-ues.
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