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In deep learning, the function approximator characterizes how the characteristics are handled to higher levels of abstraction (a fortiori can therefore give certain characteristics more or less weight). In the first levels of a deep neural network, for example, if there is an attention system, the mapping made up of those first layers can be used as a framework for selecting features. On the other hand, an asymptotic bias can occur if the function approximator used for the weighted sum and/or the rule and/or template is too basic. But on the other hand, there would be a significant error due to the limited size of the data (over fitting) when the feature approximator has weak generalization.

An especially better decision of a model-based or model-free method identified as a leading function approximator choice may infer that the state’s y-coordinate is less essential than the x-coordinate, and generalize that to the rule. It is helpful to share a performant function approximator in either a model-free or a model-based approach depending on the mission. Therefore the option to focus more on one or the other method is also a key factor in improving generalization [13, 19].

One solution to eliminating non-informative characteristics is to compel the agent to acquire a set of symbolic rules tailored to the task and to think on a more extreme scale. This abstract level logic and increased generalization have the potential to activate cognitive high-level functions such as analogical reasoning and cognitive transition. For example, the feature area of environmental may integrate a relational learning system and thus extend the notion of contextual reinforcement learning.