Reinforcement Learning Tutorial Part 1: Core Concepts Explained

This article introduces the fundamental concepts of reinforcement learning, covering the agent‑environment interaction, key terminology, reward structures, task types, policies, value functions, the Bellman equations, and how optimal strategies are derived and approximated in practice.

Bellman equationMarkov Decision ProcessOptimal Policy

0 likes · 13 min read

Reinforcement Learning Tutorial Part 1: Core Concepts Explained

DataFunSummit

Apr 2, 2024 · Artificial Intelligence

Reinforcement Learning: Fundamentals, Classic Algorithms, and Applications in Short Video Recommendation

This article provides an in-depth overview of reinforcement learning, covering its goals, mathematical foundations such as Markov Decision Processes, classic algorithms like DQN, and practical applications including short‑video recommendation systems that aim to improve user retention through RL‑based ranking.

DQNMarkov Decision ProcessRL applications

0 likes · 12 min read

Reinforcement Learning: Fundamentals, Classic Algorithms, and Applications in Short Video Recommendation

Model Perspective

Dec 28, 2022 · Artificial Intelligence

What Is Reinforcement Learning? Core Concepts and Key Algorithms Explained

This article introduces reinforcement learning, compares it with supervised and unsupervised learning, explains its components and Markov Decision Processes, and reviews fundamental model‑free and model‑based algorithms such as Q‑Learning, SARSA, TD learning, and exploration strategies.

Markov Decision ProcessQ-Learningsarsa

0 likes · 16 min read

What Is Reinforcement Learning? Core Concepts and Key Algorithms Explained