Provably Efficient Reinforcement Learning With Linear Function Approximation Chi Jin

By Editorial Team · October 18, 2026 · 6 min read

Provably Efficient Reinforcement Learning With Linear Function Approximation Chi Jin Information Guide

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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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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 21: Foundations of Reinforcement Learning: Partially Observable Reinforcement Learning I
Lecture 21: Foundations of Reinforcement Learning: Partially Observable Reinforcement Learning I
RL Theory Seminar: Chi Jin
RL Theory Seminar: Chi Jin
Function Approximation | Reinforcement Learning Part 5
Function Approximation | Reinforcement Learning Part 5
Lightning Talks - Chi Jin, Lin Yang, Alec Koppel, Karan Singh, Nataly Brukhim
Lightning Talks - Chi Jin, Lin Yang, Alec Koppel, Karan Singh, Nataly Brukhim
Lecture 6: Foundations of Reinforcement Learning: Generative Models
Lecture 6: Foundations of Reinforcement Learning: Generative Models
Chi Jin-Talk Title: When Is Partially Observable Reinforcement Learning Not Scary
Chi Jin-Talk Title: When Is Partially Observable Reinforcement Learning Not Scary
Lecture 4: Foundations of Reinforcement Learning: Concentration Inequalities
Lecture 4: Foundations of Reinforcement Learning: Concentration Inequalities
Lecture 12: Foundations of Reinforcement Learning: Offline RL
Lecture 12: Foundations of Reinforcement Learning: Offline RL

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Last Updated: October 18, 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 Reach out to us :) truetheta.io Here, we learn about Talk Abstract: Partially observability is ubiquitous in applications of

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