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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 1176–1200 of 1918 papers

TitleStatusHype
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States—0
Model-Augmented Q-learning—0
Revisiting Prioritized Experience Replay: A Value PerspectiveCode0
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
A review of motion planning algorithms for intelligent robotics—0
Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images—0
A step toward a reinforcement learning de novo genome assembler—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
QoS-Aware Power Minimization of Distributed Many-Core Servers using Transfer Q-Learning—0
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better PerformanceCode0
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation—0
Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems—0
Reinforcement Learning Assisted Beamforming for Inter-cell Interference Mitigation in 5G Massive MIMO Networks—0
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning—0
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach—0
Solving optimal stopping problems with Deep Q-Learning—0
Fire Threat Detection From Videos with Q-Rough Sets—0
Breaking the Deadly Triad with a Target Network—0
Reinforcement learning based recommender systems: A survey—0
Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time SystemsCode0
Learning Augmented Index Policy for Optimal Service Placement at the Network Edge—0
Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs—0
Safe Coupled Deep Q-Learning for Recommendation Systems—0
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity—0
Success-Rate Targeted Reinforcement Learning by Disorientation Penalty—0
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