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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 41–50 of 1918 papers

Title	Date	Tasks	Status	Hype
Boosting Continuous Control with Consistency Policy	Oct 10, 2023	continuous-controlContinuous Control	CodeCode Available	1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem	Dec 8, 2020	Combinatorial OptimizationQ-Learning	CodeCode Available	1
Reinforcement Learning in High-frequency Market Making	Jul 14, 2024	Q-Learningreinforcement-learning	CodeCode Available	1
Continuous control with deep reinforcement learning	Sep 9, 2015	Action Detectioncontinuous-control	CodeCode Available	1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning	Sep 22, 2023	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	1
Deep Active Inference for Partially Observable MDPs	Sep 8, 2020	Deep Reinforcement LearningQ-Learning	CodeCode Available	1
Deep Reinforcement Learning with Double Q-learning	Sep 22, 2015	Atari GamesDeep Reinforcement Learning	CodeCode Available	1
Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection	Sep 29, 2021	Q-LearningTraffic Signal Control	CodeCode Available	1
A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning	May 27, 2024	Data AugmentationQ-Learning	CodeCode Available	1
An Optimistic Perspective on Offline Deep Reinforcement Learning	Jan 1, 2020	Atari GamesDeep Reinforcement Learning	CodeCode Available	1

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