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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 951–960 of 1918 papers

TitleStatusHype
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-EnsembleCode1
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning—0
Learning the Markov Decision Process in the Sparse Gaussian EliminationCode1
Learning Explicit Credit Assignment for Multi-agent Joint Q-learning—0
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach—0
Q-Learning Scheduler for Multi-Task Learning through the use of Histogram of Task Uncertainty—0
Unifying Top-down and Bottom-up for Recurrent Visual Attention—0
Value Refinement Network (VRN)—0
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