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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 1161–1170 of 1918 papers

TitleStatusHype
Is Q-Learning Provably Efficient? An Extended Analysis—0
Is Risk-Sensitive Reinforcement Learning Properly Resolved?—0
"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications—0
Joint Inference of Reward Machines and Policies for Reinforcement Learning—0
Joint Learning of Interactive Spoken Content Retrieval and Trainable User Simulator—0
Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics—0
Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications—0
KAN v.s. MLP for Offline Reinforcement Learning—0
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes—0
Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine—0
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