Naive Augmenting Q-Learning to Process Feature-Based Representations of States
نویسنده
چکیده
Temporal difference algorithms perform well on discrete and small problems. This paper proposes a modification of the Q-learning algorithm towards natural ability to receive a feature list instead of an already identified state in the input. Complete observability is still assumed. The algorithm, Naive Augmenting Q-Learning, has been designed through building a hierarchical structure of input features (a kind of feature-state mapping) to avoid a direct state identification, thus potentially optimizing the required resources for storing and processing action values.
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