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In multi-agent reinforcement learning experiments, researchers try to optimize the performance of a learning agent on a given task, in cooperation or competition with one or more agents. These agents learn by trial-and-error, and researchers may choose to have the learning algorithm play the role of two or more of the different agents.
Traditional machine learning systems have a fixed, pre-programmed learning algorithm to adjust their parameters. However, since the 1980s Jürgen Schmidhuber has published several self-modifying systems with the ability to change their own learning algorithm.
Self-learning in neural networks was introduced in 1982 along with a neural network capable of self-learning named crossbar adaptive array (CAA). [139] It is a system with only one input, situation s, and only one output, action (or behavior) a. It has neither external advice input nor external reinforcement input from the environment.
Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.
Scalable Vector Graphics are well suited to simple geometric images, while photographs do not fare well with vectorization due to their complexity. Note that the special characteristics of vectors allow for greater resolution example images. The other algorithms are standardized to a resolution of 160x160 and 218x80 pixels respectively.
Autoassociative self-supervised learning is a specific category of self-supervised learning where a neural network is trained to reproduce or reconstruct its own input data. [8] In other words, the model is tasked with learning a representation of the data that captures its essential features or structure, allowing it to regenerate the original ...
The goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns. This is partly motivated by how visual, auditory or other sensory information is handled in separate parts of the cerebral cortex in the human brain. [6] An illustration of the training of a self-organizing map.
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.