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Where can I find the proof of the universal approximation theorem?

There are multiple papers on the topic because there have been multiple attempts to prove that neural networks are universal (i.e. they can approximate any continuous function) from slightly different ...
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Are there other approaches to deal with variable action spaces?

Does anyone know any paper regarding this subject? I'm not familiar with any off the top of my head. I do know that the vast majority of Reinforcement Learning literature focuses on settings with a ...
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• 33.8k

What is the relation between the context in contextual bandits and the state in reinforcement learning?

Conceptually, in general, how is the context being handled in CB, compared to states in RL? In terms of its place in the description of Contextual Bandits and Reinforcement Learning, context in CB is ...
• 23.9k
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Why are only neural networks (and not SVMs, for example) used for reinforcement learning?

The biggest problem with SVMs, random forests, gradient boosting and others for reinforcement learning (RL) is that they are not able to learn online, adjusting for new data as it arrives, and equally ...
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Can a neural network with linear activation functions produce a connection of linear functions?

Since I can't comment, there are a few caveats to previous answers. For instance, if you knew beforehand what the expected boundary function for that variable was, then you could transform it first. ...
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