# Artificial Intelligence Stack Exchange Community Digest

## Top new questions this week:

### What is the difference between Q-learning, Deep Q-learning and Deep Q-network?

Q-learning uses a table to store all state-action pairs. Q-learning is a model-free RL algorithm, so how could there be the one called Deep Q-learning, as deep means using DNN; or maybe the state-...

reinforcement-learning comparison q-learning dqn deep-rl

### Why is my GAN more unstable with bigger networks?

I am working with generative adversarial networks (GANs) and one of my aims at the moment is to reproduce samples in two dimensions that are distributed according to a circle (see animation). When ...

### Do AlphaZero/MuZero learn faster in terms of number of games played than humans?

I don't know much about AI and am just curious. From what I read, AlphaZero/MuZero outperform any human chess player after a few hours of training. I have no idea how many chess games a very talented ...

comparison training alphazero chess muzero

### How should we regularize an LSTM model?

There are five parameters from an LSTM layer for regularization if I am correct. To deal with overfitting, I would start with reducing the layers reducing the hidden units Applying dropout or ...

recurrent-neural-networks long-short-term-memory overfitting regularization dropout

### How should I implement the state transition when it is a Gaussian distribution?

I am reading this paper Anxiety, Avoidance and Sequential Evaluation and is confused about the implementation of a specific lab study. Namely, the authors model what is called the Balloon task using a ...

reinforcement-learning markov-decision-process implementation temporal-difference-methods transition-model

### Does DQN generalise to unseen states in the case of discrete state-spaces?

In my understanding, DQN is useful because it utilises a neural network as a q-value function approximator, which, after the training, can generalise to unseen states. I understand how that would work ...

reinforcement-learning dqn deep-rl generalization discrete-state-spaces

### Is non-negative matrix factorization for machine learning obsolete?

I am taking a course about using matrix factorization for machine learning. The first thing that came into my mind is by using the matrix factorization we are always limited to linear relationships ...

neural-networks machine-learning reference-request applications

## Greatest hits from previous weeks:

### In a CNN, does each new filter have different weights for each input channel, or are the same weights of each filter used across input channels?

My understanding is that the convolutional layer of a convolutional neural network has four dimensions: input_channels, filter_height, filter_width, number_of_filters. Furthermore, it is my ...

deep-learning convolutional-neural-networks image-recognition

### What is an objective function?

Local search algorithms are useful for solving pure optimization problems, in which the aim is to find the best state according to an objective function. My question is what is the objective function?

terminology loss-functions optimization local-search meta-heuristics

### How does LSTM in deep reinforcement learning differ from experience replay?

In the paper Deep Recurrent Q-Learning for Partially Observable MDPs, the author processed the Atari game frames with an LSTM layer at the end. My questions are: How does this method differ from the ...

reinforcement-learning long-short-term-memory deep-rl comparison experience-replay

### What is the difference between strong-AI and weak-AI?

I've heard the terms strong-AI and weak-AI used. Are these well defined terms or subjective ones? How are they generally defined?

terminology definitions agi comparison weak-ai

### How do I choose the best algorithm for a board game like checkers?

How do I choose the best algorithm for a board game like checkers? So far, I have considered only three algorithms, namely, minimax, alpha-beta pruning, and Monte Carlo tree search (MCTS). Apparently,...

game-ai applications monte-carlo-tree-search minimax alpha-beta-pruning

### Teach a Neural Network to play a card game

I am currently writing an engine to play a card game, as there is no engine yet for this particular game. I am hoping to be able to introduce a neural net to the game afterwards, and have it learn to ...

neural-networks machine-learning gaming neat

### How could artificial intelligence harm us?

We often hear that artificial intelligence may harm or even kill humans, so it might prove dangerous. How could artificial intelligence harm us?

philosophy social neo-luddism

## Can you answer these questions?

### Are there any approaches to AGI that will definitely not work?

Is there empirical evidence that some approaches to achieving AGI will definitely not work? For the purposes of the question the system should at least be able to learn and solve novel problems. Some ...

agi

### CSP heuristic to simultaneously reduce conflicts and find near optimal assignment

I am trying to design a good heuristic to solve a constraint satisfaction problem (CSP). I think that a possible heuristic to use is $$h_1(\text{state}) = \text{number of conflicts in state}$$ However,...

heuristics constraint-satisfaction-problems local-search
 asked by bobby brown 1 vote

### What are some of the main high level approaches to applying ML on kinematic sensor data?

I've just started a project which will involve having to detect certain events in a stream of kinematic sensor data. By searching through the literature, I've found a lot of highly specific papers, ...

machine-learning reference-request signal-processing