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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 851–875 of 1918 papers

TitleStatusHype
A Learning Based Framework for Handling Uncertain Lead Times in Multi-Product Inventory Management—0
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity—0
Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation—0
Autonomous Warehouse Robot using Deep Q-Learning—0
PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning—0
UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations—0
Goal Recognition as Reinforcement LearningCode0
Artificial Intelligence and Auction Design—0
Microservice Deployment in Edge Computing Based on Deep Q LearningCode1
Regularized Q-learning—0
Transferred Q-learning—0
Intelligent Autonomous Intersection Management—0
Multiple Correlated Jammers Nullification using LSTM-based Deep Dueling Neural Network—0
Stochastic Gradient Descent with Dependent Data for Offline Reinforcement Learning—0
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise—0
Deep Q-learning: a robust control approachCode0
Optimal variance-reduced stochastic approximation in Banach spaces—0
Deep Reinforcement Learning with Spiking Q-learning—0
Addressing Maximization Bias in Reinforcement Learning with Two-Sample TestingCode1
A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Task Independent Capsule-Based Agents for Deep Q-Learning—0
Age-of-information minimization via opportunistic sampling by an energy harvesting source—0
Sales Time Series Analytics Using Deep Q-Learning—0
Reinforcement Learning for Task Specifications with Action-Constraints—0
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