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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
Deep Surrogate Q-Learning for Autonomous Driving—0
Bootstrapping Expectiles in Reinforcement Learning—0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
Deep Reinforcement Learning for Dynamic Band Switch in Cellular-Connected UAV—0
A study on a Q-Learning algorithm application to a manufacturing assembly problem—0
Deep Primal-Dual Reinforcement Learning: Accelerating Actor-Critic using Bellman Duality—0
A review of motion planning algorithms for intelligent robotics—0
Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement—0
Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics—0
Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net—0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
Asymptotic regularity of a generalised stochastic Halpern scheme with applications—0
Asymptotics of Reinforcement Learning with Neural Networks—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
Unsynchronized Decentralized Q-Learning: Two Timescale Analysis By Persistence—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
Bootstrapped Hindsight Experience replay with Counterintuitive Prioritization—0
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