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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 1771–1780 of 1918 papers

Title	Date	Tasks	Status	Hype
Revisiting Prioritized Experience Replay: A Value Perspective	Feb 5, 2021	Atari GamesQ-Learning	CodeCode Available	0
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning	Dec 18, 2017	Deep Reinforcement LearningEvolutionary Algorithms	CodeCode Available	0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning	Mar 2, 2023	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	0
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective	Dec 2, 2018	Atari GamesQ-Learning	CodeCode Available	0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings	Oct 29, 2020	Change Point DetectionOff-policy evaluation	CodeCode Available	0
Goal-Conditioned Q-Learning as Knowledge Distillation	Aug 28, 2022	Knowledge DistillationQ-Learning	CodeCode Available	0
Reward Delay Attacks on Deep Reinforcement Learning	Sep 8, 2022	Deep Reinforcement LearningQ-Learning	CodeCode Available	0
Goal Recognition as Reinforcement Learning	Feb 13, 2022	Q-Learningreinforcement-learning	CodeCode Available	0
Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective	Jun 29, 2023	Feature EngineeringQ-Learning	CodeCode Available	0
DeepTPI: Test Point Insertion with Deep Reinforcement Learning	Jun 7, 2022	Deep Reinforcement LearningGraph Neural Network	CodeCode Available	0

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