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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 421–430 of 1918 papers

TitleStatusHype
How to discretize continuous state-action spaces in Q-learning: A symbolic control approach—0
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function ApproximationCode0
Approximate Global Convergence of Independent Learning in Multi-Agent Systems—0
Q-learning as a monotone scheme—0
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost—0
Highway Reinforcement Learning—0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
Mutation-Bias Learning in Games—0
AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained OptimizationCode0
Analysis of Multiscale Reinforcement Q-Learning Algorithms for Mean Field Control Games—0
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