Top new questions this week:
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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-...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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Greatest hits from previous weeks:
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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 ...
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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?
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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 ...
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I've heard the terms strong-AI and weak-AI used. Are these well defined terms or subjective ones? How are they generally defined?
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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,...
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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 ...
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We often hear that artificial intelligence may harm or even kill humans, so it might prove dangerous.
How could artificial intelligence harm us?
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Can you answer these questions?
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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 ...
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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,...
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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, ...
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