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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 1201–1210 of 1918 papers

Title	Date	Tasks	Status	Hype
Stabilizing Transformer-Based Action Sequence Generation For Q-Learning	Oct 23, 2020	Q-LearningReinforcement Learning (RL)	—Unverified	0
Deep Surrogate Q-Learning for Autonomous Driving	Oct 21, 2020	Autonomous DrivingDeep Reinforcement Learning	—Unverified	0
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets	Oct 21, 2020	Keypoint DetectionQ-Learning	—Unverified	0
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality	Oct 21, 2020	Multi-agent Reinforcement LearningQ-Learning	—Unverified	0
Logistic Q-Learning	Oct 21, 2020	Q-LearningReinforcement Learning (RL)	—Unverified	0
Language Inference with Multi-head Automata through Reinforcement Learning	Oct 20, 2020	Q-Learningreinforcement-learning	—Unverified	0
Learning Dexterous Manipulation from Suboptimal Experts	Oct 16, 2020	Offline RLQ-Learning	—Unverified	0
Multi-Agent Collaboration via Reward Attribution Decomposition	Oct 16, 2020	Dota 2Multi-agent Reinforcement Learning	CodeCode Available	1
A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning	Oct 15, 2020	Deep Reinforcement LearningQ-Learning	—Unverified	0
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control	Oct 13, 2020	Model-based Reinforcement LearningQ-Learning	—Unverified	0

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