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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 1651–1660 of 1918 papers

TitleStatusHype
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement LearningCode0
On Solving the 2-Dimensional Greedy Shooter Problem for UAVsCode0
Q-Learning Lagrange Policies for Multi-Action Restless BanditsCode0
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive EnvironmentsCode0
ADDQ: Adaptive Distributional Double Q-LearningCode0
Learning To Play Atari Games Using Dueling Q-Learning and Hebbian PlasticityCode0
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality TighteningCode0
Using deep Q-learning to understand the tax evasion behavior of risk-averse firmsCode0
On the Estimation Bias in Double Q-LearningCode0
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge EvolutionCode0
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