Questions tagged [function-approximation]

For questions related to the concept of function approximation. For example, questions that involve the use of a neural network (which is a function approximator) in the context of RL in order to approximate a value function or questions that are related to universal approximation theorems.

Filter by
Sorted by
Tagged with
10
votes
2answers
253 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 regarding the learning rate are satisfied $\sum_{t} \alpha_t(s, a) = \infty$ $...
6
votes
2answers
538 views

Is it possible to implement reinforcement learning using a neural network?

I've implemented the reinforcement learning algorithm for an agent to play snappy bird (a shameless cheap ripoff of flappy bird) utilizing a q-table for storing the history for future lookups. It ...
5
votes
2answers
109 views

Why don't people use projected Bellman error with deep neural networks?

Projected Bellman error has shown to be stable with linear function approximation. The technique is not at all new. I can only wonder why this technique is not adopted to use with non-linear function ...
4
votes
2answers
91 views

Can we optimize an optimization algorithm?

In this answer to the question Is an optimization algorithm equivalent to a neural network?, the author stated that, in theory, there is some recurrent neural network that implements a given ...
4
votes
1answer
121 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 ...
3
votes
1answer
60 views

Why can't neural networks learn functions outside of the specified domains?

I understand that neural nets are fundamentally interpolative tools. Meaning, given a training dataset, a well trained neural net can approximate values within the domain of the training dataset. ...
3
votes
1answer
36 views

How do I represent a multi-dimensional state using a neural network?

I have a set of 15 unique playing cards from a deck of 52 playing cards. A given state is represented by the respective card values in the set of 15 cards, where the card value is a prime number ...
3
votes
3answers
234 views

Which neural network should I use to approximate a specific function?

We have convolutional neural networks and recurrent neural networks for analysing respectively images and sequential data. How do I determine which neural network architecture is more appropriate to ...
3
votes
3answers
311 views

Which functions can't neural networks learn efficiently?

There are a lot of papers that show that neural networks can approximate a wide variety of functions. However, I can't find papers that show the limitations of NNs. What are the limitations of ...
3
votes
0answers
28 views

Convergence of Semi gradient TD(0) with non-linear function approximation

I am looking for a result that shows convergence of semi-gradient TD(0) algorithm with non-linear function approximation for on-policy prediction. Specifically, the update equation is given by (...
3
votes
0answers
21 views

Is there any open source implementation of the SBEED learning algorithm?

Are there are any openly available implementations of the SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation learning?
3
votes
0answers
72 views

What characteristics make it difficult for a Neural Network to approximate a function?

What are the characteristics which make a function difficult for the Neural Network to approximate? Intuitively, one might think uneven functions might be difficult to approximate, but uneven ...
2
votes
2answers
94 views

Why is there a sigmoid function in the hidden layer of a neural network? [duplicate]

I got this slide from CMU's lecture notes. The $x_i$s on the right are inputs and the $w_i$s are weights that get multiplied together then summed up at each hidden layer node. So I'm assuming this is ...
2
votes
2answers
55 views

Returning function that a neural network estimated

As stated in the Universal approximation theorem, a neural network can approximate almost any function. I was wondering, if there are methods to return the actual formula of the function that the NN ...
2
votes
1answer
36 views

Can supervised learning be recast as reinforcement learning problem?

Let's assume that there is a sequence of pairs $(x_i, y_i), (x_{i+1}, y_{i+1}), \dots$ of observations and corresponding labels. Let's also assume that the $x$ is considered as independent variable ...
2
votes
1answer
26 views

Changes in flow detection neural network?

Do you have any advice, what architecture of neural network is the best for following task? Let input be some (complex function), the neural network gains a flow of its values, so I guess there will ...
2
votes
1answer
543 views

Why use semi-gradient instead of full gradient in RL problems, when using function approximation?

Semi-gradient methods work well in reinforcement learning, but what is there a reason of not using the true gradient if it can be computed? I tried it on the cart pole problem with a deep Q-network ...
1
vote
0answers
49 views

Hashed Tile Coding vs Regular Tile Coding

In the book "Reinforcement Learning: An Introduction" (2018) Sutton and Barto explain at page 221 a form of tile coding using hashing, to reduce memory consumption. I have two questions about that: ...
1
vote
1answer
153 views

Which machine learning models are universal function approximators?

The universal approximation theorem states that a feed-forward neural network with a single hidden layer containing a finite number of neurons can approximate a wide variety of interesting (...
0
votes
1answer
299 views

State aggregation methods

In Sutton's RL:An introduction 2nd edition it says the following(page 203): State aggregation is a simple form of generalizing function approximation in which states are grouped together, with ...
0
votes
1answer
29 views

Tweaking a CNN for large number of input channels

I am using a CNN for function approximation using geospatial data. The input of the function I am trying to approximate consists of all the spatial distances between N location on a grid and all the ...