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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 1601–1625 of 1918 papers

TitleStatusHype
Stochastic approximation with cone-contractive operators: Sharp _-bounds for Q-learningCode0
Reinforcement Learning for Robotics and Control with Active Uncertainty Reduction—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
Domain Adversarial Reinforcement Learning for Partial Domain Adaptation—0
A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem—0
Pretrain Soft Q-Learning with Imperfect Demonstrations—0
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning—0
Accelerated Target Updates for Q-learning—0
Deep Ordinal Reinforcement LearningCode0
Comprehensible Context-driven Text Game PlayingCode0
Efficient Model-free Reinforcement Learning in Metric SpacesCode0
Soft Q-Learning with Mutual-Information Regularization—0
Learning agents with prioritization and parameter noise in continuous state and action space—0
Two-Timescale Networks for Nonlinear Value Function Approximation—0
A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks—0
Zap Q-Learning for Optimal Stopping Time Problems—0
Target-Based Temporal Difference Learning—0
Stochastic Lipschitz Q-Learning—0
Deep Q-Learning for Nash Equilibria: Nash-DQNCode0
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning—0
Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net—0
"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications—0
Patchwork: A Patch-wise Attention Network for Efficient Object Detection and Segmentation in Video Streams—0
Personalized Cancer Chemotherapy Schedule: a numerical comparison of performance and robustness in model-based and model-free scheduling methodologies—0
Learning Automata Based Q-learning for Content Placement in Cooperative Caching—0
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