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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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1271–1280 of 1918 papers

Title	Date	Tasks	Status	Hype
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning	Jul 9, 2020	Deep Reinforcement LearningDiversity	CodeCode Available	1
The Mean-Squared Error of Double Q-Learning	Jul 9, 2020	Q-Learning	CodeCode Available	0
Neural Interactive Collaborative Filtering	Jul 4, 2020	Collaborative FilteringMeta-Learning	CodeCode Available	1
Reward Machines for Cooperative Multi-Agent Reinforcement Learning	Jul 3, 2020	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Hedging using reinforcement learning: Contextual k-Armed Bandit versus Q-learning	Jul 3, 2020	FrictionQ-Learning	—Unverified	0
Group Equivariant Deep Reinforcement Learning	Jul 1, 2020	Deep Reinforcement LearningInductive Bias	CodeCode Available	0
Regularly Updated Deterministic Policy Gradient Algorithm	Jul 1, 2020	MuJoCoQ-Learning	—Unverified	0
Gradient Temporal-Difference Learning with Regularized Corrections	Jul 1, 2020	Q-Learning	CodeCode Available	1
Provably More Efficient Q-Learning in the One-Sided-Feedback/Full-Feedback Settings	Jun 30, 2020	Q-Learning	—Unverified	0
Using Reinforcement Learning to Herd a Robotic Swarm to a Target Distribution	Jun 29, 2020	Q-Learningreinforcement-learning	—Unverified	0

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