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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 101–110 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Neural Interactive Collaborative Filtering	Jul 4, 2020	Collaborative FilteringMeta-Learning	CodeCode Available	1	5
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem	Dec 8, 2020	Combinatorial OptimizationQ-Learning	CodeCode Available	1	5
Deep Recurrent Q-Learning for Partially Observable MDPs	Jul 23, 2015	Atari GamesDeep Reinforcement Learning	CodeCode Available	1	5
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization	Mar 28, 2023	D4RLOffline RL	CodeCode Available	1	5
Conservative Q-Learning for Offline Reinforcement Learning	Jun 8, 2020	continuous-controlContinuous Control	CodeCode Available	1	5
Optimal Market Making by Reinforcement Learning	Apr 8, 2021	Q-Learningreinforcement-learning	CodeCode Available	1	5
Continuous control with deep reinforcement learning	Sep 9, 2015	Action Detectioncontinuous-control	CodeCode Available	1	5
Optimization of Molecules via Deep Reinforcement Learning	Oct 19, 2018	Deep Reinforcement LearningMolecular Graph Generation	CodeCode Available	1	5
Continuous Deep Q-Learning with Model-based Acceleration	Mar 2, 2016	continuous-controlContinuous Control	CodeCode Available	1	5
An Optimistic Perspective on Offline Deep Reinforcement Learning	Jan 1, 2020	Atari GamesDeep Reinforcement Learning	CodeCode Available	1	5

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