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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 1071–1080 of 1918 papers

TitleStatusHype
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing ProblemCode0
Bootstrapped Meta-LearningCode0
User Tampering in Reinforcement Learning Recommender Systems—0
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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