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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 1126–1150 of 1918 papers

TitleStatusHype
Reputation Bootstrapping for Composite Services using CP-nets—0
A Comparison of Reward Functions in Q-Learning Applied to a Cart Position ProblemCode0
Verification of Dissipativity and Evaluation of Storage Function in Economic Nonlinear MPC using Q-Learning—0
Deep Reinforcement Learning for Optimal Stopping with Application in Financial EngineeringCode0
Online Adaptive Optimal Control Algorithm Based on Synchronous Integral Reinforcement Learning With Explorations—0
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization—0
Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems—0
Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare—0
Interpretable performance analysis towards offline reinforcement learning: A dataset perspective—0
Fast constraint satisfaction problem and learning-based algorithm for solving Minesweeper—0
Reinforcement Learning with Expert Trajectory For Quantitative Trading—0
Survey on Multi-Agent Q-Learning frameworks for resource management in wireless sensor network—0
Robotic Surgery With Lean Reinforcement LearningCode0
Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action TasksCode0
CARL-DTN: Context Adaptive Reinforcement Learning based Routing Algorithm in Delay Tolerant Network—0
RP-DQN: An application of Q-Learning to Vehicle Routing Problems—0
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks—0
Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems—0
Low-rank State-action Value-function ApproximationCode0
A Simulated Experiment to Explore Robotic Dialogue Strategies for People with Dementia—0
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills—0
Prospect-theoretic Q-learning—0
Autoequivariant Network Search via Group DecompositionCode0
Towards Resilience for Multi-Agent QD-Learning—0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing—0
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