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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 261–270 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Angrier Birds: Bayesian reinforcement learning	Jan 6, 2016	Efficient ExplorationQ-Learning	CodeCode Available	0	5
DeepTPI: Test Point Insertion with Deep Reinforcement Learning	Jun 7, 2022	Deep Reinforcement LearningGraph Neural Network	CodeCode Available	0	5
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation	Jan 9, 2018	Autonomous DrivingAutonomous Navigation	CodeCode Available	0	5
Deep Reinforcement Learning with a Natural Language Action Space	Nov 14, 2015	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5
Active inference: demystified and compared	Sep 24, 2019	Atari GamesOpenAI Gym	CodeCode Available	0	5
Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks	Mar 29, 2018	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5
Deep reinforcement learning for time series: playing idealized trading games	Mar 11, 2018	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods	Feb 28, 2018	Deep Reinforcement LearningDiversity	CodeCode Available	0	5
Deep Reinforcement Learning for Multi-class Imbalanced Training	May 24, 2022	Deep Reinforcement Learningimbalanced classification	CodeCode Available	0	5
Action Candidate Driven Clipped Double Q-learning for Discrete and Continuous Action Tasks	Mar 22, 2022	Q-Learning	CodeCode Available	0	5

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