Deep Q-Networks (DQNs)

Oct 6, 2022 ... Our results show that synergy increases as neural networks learn multiple diverse tasks. Furthermore, performance in tasks requiring integration ... Feb 24, 2020 ... We show how to encode both types of domain knowledge into the widely used Gaussian scale mixture priors with Automatic Relevance Determination. Apr 8, 2025 ... Neural networks are computational models that learn how to recognize patterns, develop predictions, and process data. By using algorithms, ... ABSTRACT. Introduction: Natural Language Processing through artificial neural networks has gaps that can be addressed by Information Science. Dec 20, 2024 ... In ecosystems, financial markets, and neural networks, entropy fluctuates as new information reshapes the probability landscape. 4.2 The ... performing the functions of associative memory. L. Figure 1. Dependence of the main neural network informational charaaeristi~ on the number of stored templates ... neuron control with light. This technology is called optogenetics. [Photo ] The most effective means to analyze information processing in the neural network is ... Sep 10, 2019 ... Using a genetic algorithm approach to find optimal neural network structures for model-free policy learning in reinforcement learning; Using ... ... neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of ... ... neural networks contribute to familiar face recognition. Original language, English. Article number, 117896. Pages (from-to), 1-15. Number of pages, 15. Journal ...