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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

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Showing 1421–1430 of 1918 papers

TitleStatusHype
Information Theoretic Model Predictive Q-Learning—0
The Gambler's Problem and Beyond—0
Learning in Discounted-cost and Average-cost Mean-field Games—0
Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time—0
Learning an Interpretable Traffic Signal Control PolicyCode0
Soft Q Network—0
Sepsis World Model: A MIMIC-based OpenAI Gym "World Model" Simulator for Sepsis Treatment—0
Provably Efficient Reinforcement Learning with Aggregated States—0
High dimensional precision medicine from patient-derived xenografts—0
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation—0
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