Understanding the Mathematical Foundations of Reinforcement Learning

This article provides a concise overview of a ten‑chapter reinforcement‑learning textbook, outlining the progression from basic concepts such as states and rewards to advanced algorithms like policy gradients and actor‑critic methods, and explains how each chapter builds on the previous ones.

Bellman equationMonte Carloactor-critic

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Understanding the Mathematical Foundations of Reinforcement Learning

AI Algorithm Path

May 27, 2025 · Artificial Intelligence

Reinforcement Learning Tutorial 8: Building State Feature Representations for Objective Optimization

This tutorial explains how to construct state feature vectors for reinforcement‑learning value‑function approximation, covering linear, polynomial, Fourier, and radial‑basis representations, as well as state aggregation techniques such as coarse coding and tile coding, and discusses non‑parametric approaches like kernel methods.

feature engineeringfourier basisfunction approximation

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Reinforcement Learning Tutorial 8: Building State Feature Representations for Objective Optimization

Model Perspective

Oct 17, 2024 · Artificial Intelligence

Visualizing How Neural Networks Approximate Any Function

This article explains the universal approximation theorem, showing how even a simple neural network with one hidden layer can approximate any continuous function by adjusting weights and biases, and illustrates the process with visual examples of step and bump functions, linking theory to recent Nobel recognitions.

AINeural Networksfunction approximation

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Visualizing How Neural Networks Approximate Any Function