Provably Efficient Reinforcement Learning With Linear Function Approximation Chi Jin

By Editorial Team · September 27, 2026 · 9 min read

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Introduction to Provably Efficient Reinforcement Learning With Linear Function Approximation Chi Jin

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Information Lecture 16: Foundations of Reinforcement Learning: General Function Approximation News
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Lecture 21: Foundations of Reinforcement Learning: Partially Observable Reinforcement Learning I
Lecture 21: Foundations of Reinforcement Learning: Partially Observable Reinforcement Learning I
Machine Learning - Reinforcement Learning - Linear Function Approximation
Machine Learning - Reinforcement Learning - Linear Function Approximation
Exploration in reinforcement learning - Chi Jin
Exploration in reinforcement learning - Chi Jin
Lecture 12: Foundations of Reinforcement Learning: Offline RL
Lecture 12: Foundations of Reinforcement Learning: Offline RL
RL Theory Seminar: Chi Jin
RL Theory Seminar: Chi Jin
Lecture 20: Foundations of Reinforcement Learning: Multiplayer General-Sum Games
Lecture 20: Foundations of Reinforcement Learning: Multiplayer General-Sum Games
Lecture 22: Foundations of Reinforcement Learning: Partially Observable Reinforcement Learning II
Lecture 22: Foundations of Reinforcement Learning: Partially Observable Reinforcement Learning II
Lecture 4: Foundations of Reinforcement Learning: Concentration Inequalities
Lecture 4: Foundations of Reinforcement Learning: Concentration Inequalities
Lecture 6: Foundations of Reinforcement Learning: Generative Models
Lecture 6: Foundations of Reinforcement Learning: Generative Models
Lecture 14: Foundations of Reinforcement Learning: Least-Squares Value Iteration
Lecture 14: Foundations of Reinforcement Learning: Least-Squares Value Iteration
Chi Jin-Talk Title: When Is Partially Observable Reinforcement Learning Not Scary
Chi Jin-Talk Title: When Is Partially Observable Reinforcement Learning Not Scary

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Last Updated: September 27, 2026

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Information Lecture 17: Foundations of Reinforcement Learning: Exploration in General Function Approximation News
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Workshop on Theory of Deep Learning: Where next? Topic: Provably Efficient Reinforcement Learning Lectures from ECE524 Foundations of This presentation demonstrates a Short talks by postdoctoral members Topic: Exploration in Talk Abstract: Partially observability is ubiquitous in applications of

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