Inductive Biases in a Reinforcement Learner,

Abstract
Reinforcement Learning Methods (RLMs) typically select candidate solutions stochastically based on a credibility space of hypotheses which the RLM maintains, either implicitly or explicitly. RLMs typically have both inductive and deductive aspects: they inductively improve their credibility space on a stage-by stage basis; they deductively select an appropriate response to incoming stimuli using their credibility space. In this sense, RLMs share some learning attributes in common with active, incremental concept learners. Unlike some concept learners that employ deterministic procedures for selecting hypotheses, however, the evaluations of hypotheses provided to RLMs are often uncertain, either due to noisy environments, or due to summary evaluations which occur after a sequence of learner environment interactions.

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