# 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.

86 questions
Filter by
Sorted by
Tagged with
100 views

### Can an RNN predict a sinus curve with no input?

I read a number of tutorials on how to make an RNN (simple, LSTM, etc.) that predicts a sinus curve. They all use as an input (x) in every step a set of past sinus values. I am wondering if ...
1 vote
26 views

### Can we use Low rank approximation in Markov decision process problem?

I am newbie in MDP.I have started reading Ronald Howard Dynamic Programming and MDP book as well as Sutton and Barto An Introduction to Reinforcement Learning. To my understanding MDP is a model based ...
58 views

### Understanding SARSA with binary features and function approximation

I'm reading the Sutton and Barto Book on Reinforcement Learning (for oral exam preparation). However, I'm stuck on the algorithm for SARSA($\lambda$) with binary features and linear function ...
1 vote
85 views

### Is there a mathematical proof that a binary neural network can approximate any function with arbitrary accuracy?

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 ...
379 views

### Why are policy gradient methods more effective in high-dimensional action spaces?

David Silver argues, in his Reinforcement Learning course, that policy-based reinforcement learning (RL) is more effective than value-based RL in high-dimensional action spaces. He points out that the ...
41 views

### Making a model predict on the basis of a particular value

I have an input vector of shape say 1 x 400. It's fed into a network that outputs a 1 x 100 vector. I want to design a model that only considers every 4th value of this tensor and gives me the max ...
1k views

### The reason behind using MCTS over Alpha Beta Pruning in Alphazero

I am not really satisfied with the available analysis of why AlphaZero uses MCTS instead of Alpha Beta search. Some analysis claim that its because MCTS is a lot more humanlike. I disagree because I ...
1 vote
66 views

### Can I minimize a mysterious function by running a gradient descent on her neural net approximations?

So I have this function let call her $F:[0,1]^n \rightarrow \mathbb{R}$ and say $10 \le n \le 100$. I want to find some $x_0 \in [0,1]^n$ such that $F(x_0)$ is as small as possible. I don't think ...
464 views

### PRNG Function Prediction [duplicate]

Can any machine learning algorithm predict the function of pseudo random number generator given only its inputs and outputs? Here, you can also assume that you don't know the seed for the PRNG.
195 views

### Why are only neural networks (and not SVMs, for example) used for reinforcement learning?

I know that neural networks are the "universal function approximator", but they also have a huge number of trainable parameters and are extremely prone to overfitting. So my question is: Why ...
323 views

### Why does the average-reward estimator for continuing tasks use the TD error?

In Sutton and Barto's RL book, section 10.3 describes how to use average reward $r(\pi)$ to define the quality of a policy, re-defining action-value function $q_\pi(s,a)$ and value function $v_\pi(s)$ ...
118 views

### Could Softmax Action Selection be useful to solve an episodic task with more than 100000 possible states and 2000 actions?

I am new in the field of RL. I am trying to use tabular methods, Q-Learning for solving a problem that takes a lot of time for computation, so I would like to know if there are more efficient methods ...
1 vote
35 views

### Knowing the futility of discounting in continuing problems, how can we say discounting has no role in control problems with function approximation?

Sutton-Barto (Section 10.4, page 254): Based on the futility of discounting in continuing problems, how can we conclude that discounting has no role to play in control problems with function ...
1 vote
36 views

### Why is the step-size $\alpha_t = 1/t$ not appropriate for non-stationary function approximation?

Sutton-Barto (Section: Selecting Step-Size Parameters Manually, page: 222) The classical choice $\alpha_t = 1/t$, which produces sample averages in tabular MC methods, is not appropriate for TD ...
65 views

### Unclear points for polynomial basis for function approximation [closed]

I have 3 questions for the following box from Sutton-Barto's RL book (page 211) on polynomial basis: Q1- Why is each $x_i$ an "order-n" polynomial? I think this is wrong: in my opinion, ...
1 vote
33 views

### What is the difference between the $Q_a$ calculated to update delta and those to select next action in the exploitation phase?

As the title suggests, I have a doubt about the computation of the $Q_a$ used to update the delta and the $Q_a$ used to select the next action in the exploitation phase, as shown below (source of ...
141 views

### Watkins' Q(λ) with function approximation: why is gradient not considered when updating eligibility traces for the exploitation phase?

I'm implementing the Watkins' Q(λ) algorithm with function approximation (in 2nd edition of Sutton & Barto). I am very confused about updating the eligibility traces because, at the beginning of ...
200 views

### Alternatives to neural networks for function approximation in Q learning?

I want to know if there is anything other than neural networks (or Deep NNs) that I can effectively use to perform function approximation? I am asking this w.r.t to the use of approximators in Q ...
134 views

### What do we actually 'approximate' when dealing with large state spaces in Q-learning?

I realized that my state space is very large in size. I had planned to use tabular Q-learning (Bellman equation to update the $Q(s, a)$ after each action taken). But this 'large space' realization has ...
146 views

### In TD(0) with linear function approximation, why is the gradient of $\hat v(S^{\prime}, \mathbf w)$ wrt parameters $\mathbf w$ not considered?

I am reading these slides. On page 38, the update for the parameters for the linear function approximation of TD(0) is given. I have a doubt regarding this. The cost function (RMSE) is given on page ...
1 vote
126 views

### Does there exist functions for which the necessary number of nodes in a shallow neural network tends to infinity as approximation error tends to 0?

The Universal Approximation Theorem states (roughly) that any continuous function can be approximated to within an arbitrary precision $\varepsilon>0$ by a feedforward neural network with one ...
43 views

### Uniform representation of images for machine learning

I'm new to the field of ML so please bear with me while I try to explain what I'm looking for. In most machine learning pipelines that deal with images there is a requirement to "normalize" ...
1 vote
671 views

### Why use sin/cos to give periodicity in time series prediction

In this tutorial https://www.tensorflow.org/tutorials/structured_data/time_series#feature_engineering (scroll down a bit to "Time" heading), they take the sin/cos of the time index, and give ...
1 vote
49 views

### Is it true that real world data is highly discontinuous?

A function $f$ is said to be continuous at a point $c$ if it satisfies three properties: Should be defined at the point $c$ Left and right-hand limits at $c$ must be equal i.e., the limit must exist ...
83 views

### Is Reinforcement Learning capable of learning complex functions (such as producing a 3d model given an image)?

I want to build an AI that can convert an image of a subject into an anatomically accurate 3D model. To do this, I was thinking of adapting the following code for Deep Deterministic Policy Gradient: ...
1 vote
36 views

### Recursive Least squares (RLS) for mini batch

For my application I am considering a learning problem where I simulate a bunch of episodes say '$n$' first, and than carry out the recursive least squares update. Similar to $TD(1)$. I know that RLS ...
1 vote
26 views

1 vote
71 views

### Is there any thumb rule on the cardinality of state space in order to use the parameterized function to estimate value functions?

Value functions for a given MDP can be learned in at least two ways by experience. The first way (tabular calculation) is generally used in the case of state spaces that are small enough. The second ...
1 vote
88 views

### Trying to understand why nonlinearity is important for neural networks by analogy

Is the reason why linear activation functions are usually pretty bad at approximating functions the same reason why combinations of hermitian polynomials or combinations of sines and cosines are ...
244 views

### Why can a neural network use more than one activation function?

From trying to understand neural networks better, I've come upon a tentative notion that an activation function aims to build a function it's approximating via linear combinations with biases and ...
768 views

### Why the optimal Bellman operator of a Q-function can be approximated by a single point

I am currently studying reinforcement learning, especially DQN. In DQN, learning proceeds in such a way as to minimize the norm (least-squares, Huber, etc.) of the optimal Bellman equation and the ...
1 vote
37 views

### How can "any process you can imagine" be thought of as function computation?

I stumbled upon this passage when reading this guide. Universality theorems are a commonplace in computer science, so much so that we sometimes forget how astonishing they are. But it's worth ...
82 views

### How to find good features for a linear function approximation in RL with large discrete state set?

I've recently read much about feature engineering in continuous (uncountable) feature spaces. Now I am interested what methods exist in the setting of large discrete state spaces. For example consider ...