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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 391–400 of 1918 papers

TitleStatusHype
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game—0
Nash Soft Actor-Critic LEO Satellite Handover Management Algorithm for Flying Vehicles—0
Scheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learning—0
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator—0
Emergence of cooperation under punishment: A reinforcement learning perspective—0
Regularized Q-Learning with Linear Function Approximation—0
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation—0
Information-Theoretic State Variable Selection for Reinforcement LearningCode0
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and TrainabilityCode0
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes—0
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