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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 121–130 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Deep Reinforcement Learning with Double Q-learning	Sep 22, 2015	Atari GamesDeep Reinforcement Learning	CodeCode Available	1	5
Robust Q-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty	Sep 30, 2022	Q-Learning	CodeCode Available	1	5
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning	Feb 16, 2021	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1	5
Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions	Oct 19, 2023	Deep Reinforcement LearningQ-Learning	CodeCode Available	1	5
Semantic Visual Navigation by Watching YouTube Videos	Jun 17, 2020	Q-LearningVisual Navigation	CodeCode Available	1	5
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning	May 31, 2021	FairnessMulti-agent Reinforcement 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
Image Classification by Reinforcement Learning with Two-State Q-Learning	Jun 28, 2020	ClassificationGeneral Classification	CodeCode Available	1	5
Multiagent Cooperation and Competition with Deep Reinforcement Learning	Nov 27, 2015	Deep Reinforcement LearningQ-Learning	CodeCode Available	1	5

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