Actor-critic algorithm
The actor-critic algorithm is a reinforcement learning method combining a policy, the actor, with a value function, the critic, introduced by Barto, Sutton, and Anderson in 1983.
List of datasets for machine-learning research
The list of datasets for machine-learning research, also called a comparison of datasets in machine learning, covers the public collections researchers use to train, evaluate, and compare algorithms, from the UCI repository to Kaggle.
Lists of open-source artificial intelligence software
Lists of open-source artificial intelligence software are curated catalogs of AI tools, libraries, frameworks, and models with open licenses, including GitHub awesome lists and platform category pages.
Machine learning
Machine learning is the branch of computer science concerned with building computers that improve automatically through experience, drawing on statistics and spanning supervised, unsupervised, and reinforcement learning.
Normalization (machine learning)
Normalization in machine learning is a family of techniques that rescale a neural network's inputs, activations, or weights during training, making gradient-based optimization faster and more stable.
One-hot
One-hot encoding, also called one-of-K or dummy encoding, represents a categorical variable as a vector with a 1 for the instance's category and 0 elsewhere.
Policy gradient method
A policy gradient method is a reinforcement learning technique that optimizes a parameterized policy directly by gradient ascent on expected return, rather than learning a value function.