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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 1011–1020 of 1918 papers

TitleStatusHype
Double Deep Q-learning Based Real-Time Optimization Strategy for Microgrids—0
Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments—0
Constraints Penalized Q-learning for Safe Offline Reinforcement Learning—0
A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens—0
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication—0
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things—0
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem—0
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy LearningCode0
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement LearningCode0
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