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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 476–500 of 1918 papers

TitleStatusHype
Agnostic Q-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity—0
Asymptotics of Reinforcement Learning with Neural Networks—0
Asymptotic regularity of a generalised stochastic Halpern scheme with applications—0
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity—0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning—0
Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net—0
Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics—0
Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement—0
A review of motion planning algorithms for intelligent robotics—0
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance—0
Deep Primal-Dual Reinforcement Learning: Accelerating Actor-Critic using Bellman Duality—0
Deep Q-Learning for Directed Acyclic Graph Generation—0
A study on a Q-Learning algorithm application to a manufacturing assembly problem—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—0
Deep Q-Learning for Same-Day Delivery with Vehicles and Drones—0
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement—0
A study of first-passage time minimization via Q-learning in heated gridworlds—0
Deep Q Learning from Dynamic Demonstration with Behavioral Cloning—0
Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market—0
Deep Q-learning of global optimizer of multiply model parameters for viscoelastic imaging—0
Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task—0
Deep Q-Learning with Gradient Target Tracking—0
Deep Q-Learning with Low Switching Cost—0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm—0
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