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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
ModelicaGym: Applying Reinforcement Learning to Modelica Models	Sep 18, 2019	Q-Learningreinforcement-learning	CodeCode Available	1
Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges: Trade-offs between Model-free Learning and A Priori Knowledge	May 26, 2020	Q-Learningreinforcement-learning	CodeCode Available	1
Multi-Agent Determinantal Q-Learning	Jun 2, 2020	Q-Learning	CodeCode Available	1
Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator	Dec 8, 2023	Model Predictive ControlMulti-agent Reinforcement Learning	CodeCode Available	1
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning	Sep 13, 2018	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Neural Interactive Collaborative Filtering	Jul 4, 2020	Collaborative FilteringMeta-Learning	CodeCode Available	1
An Optimistic Perspective on Offline Deep Reinforcement Learning	Jan 1, 2020	Atari GamesDeep Reinforcement Learning	CodeCode Available	1
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization	Mar 28, 2023	D4RLOffline RL	CodeCode Available	1
Optimal Market Making by Reinforcement Learning	Apr 8, 2021	Q-Learningreinforcement-learning	CodeCode Available	1
A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks	Feb 6, 2020	energy managementenergy trading	CodeCode Available	1

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