Optimal Behavior is Easier to Learn than the Truth.
We consider a reinforcement learning setting where the learner is given a set of possible models containing the true model. While there are algorithms that are able to successfully learn optimal behavior in this setting, they do so without trying to identify the underlying true model. Indeed, we show that there are cases in which the attempt to find the true model is doomed to failure.
Department Mathematik und Informationstechnologie, Montanuniversität Leoben, Franz-Josef-Straße 18, 8700 Leoben, Austria.
Ronald
R
Ortner
Markov decision processes; Regret; Reinforcement learning; Truth
Cogn Psychol. 2004 Aug;49(1):47-84
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