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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 1311–1320 of 1918 papers

Title	Date	Tasks	Status	Hype
DRIFT: Deep Reinforcement Learning for Functional Software Testing	Jul 16, 2020	Deep Reinforcement LearningGraph Neural Network	—Unverified	0
Meta-Gradient Reinforcement Learning with an Objective Discovered Online	Jul 16, 2020	Deep Reinforcement LearningQ-Learning	—Unverified	0
Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning	Jul 15, 2020	Deep Reinforcement LearningQ-Learning	—Unverified	0
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent	Jul 15, 2020	Atari GamesQ-Learning	—Unverified	0
Single-partition adaptive Q-learning	Jul 14, 2020	Q-LearningReinforcement Learning (RL)	CodeCode Available	0
Revisiting Fundamentals of Experience Replay	Jul 13, 2020	Deep Reinforcement LearningDQN Replay Dataset	CodeCode Available	0
The Mean-Squared Error of Double Q-Learning	Jul 9, 2020	Q-Learning	CodeCode Available	0
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

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