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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 61–70 of 1918 papers

Title	Date	Tasks	Status	Hype
Mildly Conservative Q-Learning for Offline Reinforcement Learning	Jun 9, 2022	D4RLQ-Learning	CodeCode Available	1
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning	May 2, 2022	Data AugmentationQ-Learning	CodeCode Available	1
GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation Learning	Apr 5, 2022	Imitation LearningQ-Learning	CodeCode Available	1
Microservice Deployment in Edge Computing Based on Deep Q Learning	Feb 11, 2022	Edge-computingQ-Learning	CodeCode Available	1
Addressing Maximization Bias in Reinforcement Learning with Two-Sample Testing	Jan 20, 2022	Q-Learningreinforcement-learning	CodeCode Available	1
Safety and Liveness Guarantees through Reach-Avoid Reinforcement Learning	Dec 23, 2021	Deep Reinforcement LearningQ-Learning	CodeCode Available	1
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives	Dec 8, 2021	Q-LearningReinforcement Learning (RL)	CodeCode Available	1
Regularized Softmax Deep Multi-Agent Q-Learning	Dec 1, 2021	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Offline Reinforcement Learning with Implicit Q-Learning	Oct 12, 2021	D4RLOffline RL	CodeCode Available	1
Dropout Q-Functions for Doubly Efficient Reinforcement Learning	Oct 5, 2021	Computational EfficiencyQ-Learning	CodeCode Available	1

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