# Tag Info

Accepted

### 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 ...
• 36.3k

### 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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• 276
Accepted

### Why are neural networks preferred to other classification functions optimized by gradient decent

You can indeed fit a polynomial to your labelled data, which is known as polynomial regression (which can e.g. be done with the function numpy.polyfit). One ...
• 36.3k
Accepted

### How do we derive the expression for average reward setting in continuing tasks?

We assume that our MDP is ergodic. Loosely speaking, this means that wherever the MDP starts (i.e. no matter which state we start in) or any actions the agent takes early on can only have a limited ...
• 4,340

### Can stochastic gradient descent be properly used in any sample based learning algorithm in Reinforcement Learning?

First I will address the issue of Tabular methods. These do not use SGD at all. Although the updates are very similar to an SGD update there is no gradient here and so we are not using SGD. Many ...
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### 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 ...
• 25.4k