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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 976–1000 of 1918 papers

TitleStatusHype
Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial DetectionCode1
On the Estimation Bias in Double Q-LearningCode0
Deep Reinforcement Learning with Adjustments—0
Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning—0
Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy GradientsCode0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning—0
Off-line approximate dynamic programming for the vehicle routing problem with a highly variable customer basis and stochastic demands—0
Search For Deep Graph Neural Networks—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing—0
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback—0
Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning—0
Deep hierarchical reinforcement agents for automated penetration testing—0
Bootstrapped Meta-LearningCode0
User Tampering in Reinforcement Learning Recommender Systems—0
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing ProblemCode0
Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
Learning-Based Strategy Design for Robot-Assisted Reminiscence Therapy Based on a Developed Model for People with Dementia—0
Event-Based Communication in Distributed Q-Learning—0
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge DistillationCode0
Deep Reinforcement Learning for Dynamic Band Switch in Cellular-Connected UAV—0
DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection—0
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