DRL Toolbox Matlab

Deep Reinforcement Learning, MatlabLeave a Comment on DRL Toolbox Matlab
“Reinforcement Learning Toolbox,” Mathworks.com, 2026. https://au.mathworks.com/products/reinforcement-learning.html?requestedDomain= (accessed Feb 23, 2026). Implementing Deep Reinforcement Learning Toolbox Defining Observation and Action Spaces The first stage of development involves establishing the operational boundaries. Data specifications must be defined for both observations (system states) and actions (available commands). rlNumericSpec([5 1]); % Define continuous state space rlFiniteSetSpec(1:4); % Define discrete action ... Read More

Time Complexity – Computer Science

Data Structure and AlgorithmLeave a Comment on Time Complexity – Computer Science
Complexity Hierarchy Logarithmic: $O(\log n)$ The “Sublinear” breakthrough. Allows processing of massive matrices by only sampling specific parts. Linear: $O(n)$ The “Old” Standard. Required reading the entire input, which is impossible for modern recommendation scales. Polynomial: $O(poly(k))$ The “Classical Analogue.” While slower than quantum, it remains fast enough to be practical. Exponential: $O(2^n)$ The “Quantum ... Read More

Machine Learning Applications – Learning Associations

Machine LearningLeave a Comment on Machine Learning Applications – Learning Associations
References: Learning Associations Association Rule Learning is a rule-based method used to discover interesting relations or “hidden patterns” between variables in large databases. It is primarily an unsupervised learning technique because it doesn’t require pre-labeled data; it simply looks for items that frequently occur together. The most famous application is Market Basket Analysis, which retailers ... Read More

Quantum Federated Learning with Qiskit

Quantum Federated LearningLeave a Comment on Quantum Federated Learning with Qiskit
Resources: What is Quantum Federated Learning? Quantum Federated Learning (QFL) is a decentralized machine learning framework where multiple clients collaboratively train a global quantum model without sharing their private, local data. In this framework, clients perform local training on their own datasets and then share only their updated model weights with a central server for ... Read More
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