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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 71–80 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
When should we prefer Decision Transformers for Offline Reinforcement Learning?	May 23, 2023	D4RLImitation Learning	CodeCode Available	1	5
A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning	May 27, 2024	Data AugmentationQ-Learning	CodeCode Available	1	5
Deep Inverse Q-learning with Constraints	Aug 4, 2020	Q-Learning	CodeCode Available	1	5
Deep Reinforcement Learning with Double Q-learning	Sep 22, 2015	Atari GamesDeep Reinforcement Learning	CodeCode Available	1	5
Continuous control with deep reinforcement learning	Sep 9, 2015	Action Detectioncontinuous-control	CodeCode Available	1	5
Discriminator Soft Actor Critic without Extrinsic Rewards	Jan 19, 2020	Imitation LearningQ-Learning	CodeCode Available	1	5
Acting in Delayed Environments with Non-Stationary Markov Policies	Jan 28, 2021	Cloud ComputingQ-Learning	CodeCode Available	1	5
Distributed Heuristic Multi-Agent Path Finding with Communication	Jun 21, 2021	Multi-Agent Path FindingQ-Learning	CodeCode Available	1	5
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models	Oct 9, 2020	Deep Reinforcement LearningEpidemiology	CodeCode Available	1	5
Conservative Q-Learning for Offline Reinforcement Learning	Jun 8, 2020	continuous-controlContinuous Control	CodeCode Available	1	5

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