Skip to main content

All Questions

Filter by
Sorted by
Tagged with
4 votes
1 answer
285 views

Why policy improvement theorem can't be applied in case of function approximation?

Policy improvement theorem is proven as follows: $$v_\pi(s) = \sum_{a \in A} \pi(a|s)q_\pi(a,s) \leq \max_{a \in A} q_\pi(a,s) = q_\pi(\pi'(s), s)$$ What step of the proof does not hold for function ...
Samuel Krempasky's user avatar
1 vote
1 answer
178 views

Is there a mathematical proof of the universal approximation theorem for neural networks with binary weights?

Since the Universal approximation theorem shows that standard multilayer feedforward networks with as few as a single hidden layer, sufficient hidden units, and arbitrary bounded and nonconstant ...
user68072's user avatar
2 votes
2 answers
514 views

Why is the equation $\mathbb{E} \left[ (Y - \hat{Y})^2 \right] = \left(f(X) - \hat{f}(X) \right)^2 + \operatorname{Var} (\epsilon)$ true?

In the book An Introduction to Statistical Learning, the authors claim (equation 2.3, p. 19, chapter 2) $$\mathbb{E} \left[ (Y - \hat{Y})^2 \right] = \left(f(X) - \hat{f}(X) \right)^2 + \operatorname{...
nbro's user avatar
  • 41.4k
2 votes
1 answer
54 views

Equivalence between expected parameter increments in "Off-Policy Temporal-Difference Learning with Function Approximation"

I am having a hard time understanding the proof of theorem 1 presented in the "Off-Policy Temporal-Difference Learning with Function Approximation" paper. Let $\Delta \theta$ and $\Delta \...
A M's user avatar
  • 23
31 votes
3 answers
18k views

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

The Wikipedia article for the universal approximation theorem cites a version of the universal approximation theorem for Lebesgue-measurable functions from this conference paper. However, the paper ...
Leroy Od's user avatar
  • 473
22 votes
3 answers
6k views

Why doesn't Q-learning converge when using function approximation?

The tabular Q-learning algorithm is guaranteed to find the optimal $Q$ function, $Q^*$, provided the following conditions (the Robbins-Monro conditions) regarding the learning rate are satisfied $\...
nbro's user avatar
  • 41.4k