"An example of simultaneously optimizing two policies for two adversarial agents, looking specifically at the cat and mouse game."
The article explores developing strategies for two players with conflicting goals, using methods like game trees, reinforcement learning, and hill-climbing optimization. The focus is on determining optimal policies for each player to either catch or evade capture, considering board configurations and player turn orders. The article further details how hill climbing is applied to improve strategies incrementally, using variations in policies to evaluate and enhance performance over numerous iterations.
The article explores developing strategies for two players with conflicting goals, using methods like game trees, reinforcement learning, and hill-climbing optimization. The focus is on determining optimal policies for each player to either catch or evade capture, considering board configurations and player turn orders. The article further details how hill climbing is applied to improve strategies incrementally, using variations in policies to evaluate and enhance performance over numerous iterations.
This article provides an overview of feature selection in machine learning, detailing methods to maximize model accuracy, minimize computational costs, and introduce a novel method called History-based Feature Selection (HBFS).
Support Vector Machine (SVM) algorithm with a focus on classification tasks, using a simple 2D dataset for illustration. It explains key concepts like hard and soft margins, support vectors, kernel tricks, and optimization probles.
Elbow curve and Silhouette plots both are very useful techniques for finding the optimal K for K-means clustering
