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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 131–140 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies	Apr 20, 2023	Offline RLQ-Learning	CodeCode Available	1	5
An Optimistic Perspective on Offline Deep Reinforcement Learning	Jan 1, 2020	Atari GamesDeep Reinforcement 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
MADiff: Offline Multi-agent Learning with Diffusion Models	May 27, 2023	Offline RLQ-Learning	CodeCode Available	1	5
Evolution Strategies as a Scalable Alternative to Reinforcement Learning	Mar 10, 2017	Atari GamesMuJoCo	CodeCode Available	1	5
Extreme Q-Learning: MaxEnt RL without Entropy	Jan 5, 2023	D4RLDeep Reinforcement Learning	CodeCode Available	1	5
Optimal Market Making by Reinforcement Learning	Apr 8, 2021	Q-Learningreinforcement-learning	CodeCode Available	1	5
A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks	Feb 6, 2020	energy managementenergy trading	CodeCode Available	1	5
GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation Learning	Apr 5, 2022	Imitation LearningQ-Learning	CodeCode Available	1	5
Table2Charts: Recommending Charts by Learning Shared Table Representations	Aug 24, 2020	Q-LearningRecommendation Systems	CodeCode Available	1	5

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