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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 1141–1150 of 1918 papers

TitleStatusHype
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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