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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 1501–1525 of 1918 papers

TitleStatusHype
Q-Learning for Continuous Actions with Cross-Entropy Guided Policies—0
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity—0
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis—0
Q-learning for Optimal Control of Continuous-time Systems—0
Q-learning for POMDP: An application to learning locomotion gaits—0
Q-learning for real time control of heterogeneous microagent collectives—0
Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments—0
q-Learning in Continuous Time—0
Q-Learning in enormous action spaces via amortized approximate maximization—0
Q-Learning in Regularized Mean-field Games—0
Q-Learning Inspired Self-Tuning for Energy Efficiency in HPC—0
Q-learning optimization in a multi-agents system for image segmentation—0
Q-learning pour la r\'esolution des anaphores pronominales en langue arabe (Q-learning for pronominal anaphora resolution in Arabic texts)—0
Q-Learning Scheduler for Multi-Task Learning through the use of Histogram of Task Uncertainty—0
Q-Learning Scheduler for Multi Task Learning Through the use of Histogram of Task Uncertainty—0
Q-learning with temporal memory to navigate turbulence—0
Q-Learning with Basic Emotions—0
Q-Learning with Clustered-SMART (cSMART) Data: Examining Moderators in the Construction of Clustered Adaptive Interventions—0
Q-Learning with Differential Entropy of Q-Tables—0
Q-learning with Logarithmic Regret—0
Q-learning with Nearest Neighbors—0
Q-learning with online random forests—0
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP—0
Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning—0
Q-MIND: Defeating Stealthy DoS Attacks in SDN with a Machine-learning based Defense Framework—0
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