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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 1461–1470 of 1918 papers

Title	Date	Tasks	Status	Hype
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning	Oct 27, 2019	Deep Reinforcement LearningImitation Learning	CodeCode Available	0
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations	Oct 26, 2019	Atari GamesDeep Reinforcement Learning	CodeCode Available	0
D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments	Oct 26, 2019	PositionQ-Learning	—Unverified	0
Deep Q-Learning for Same-Day Delivery with Vehicles and Drones	Oct 25, 2019	Decision MakingQ-Learning	—Unverified	0
Momentum-based Accelerated Q-learning	Oct 23, 2019	Atari GamesQ-Learning	CodeCode Available	0
Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation	Oct 23, 2019	Q-LearningReinforcement Learning	—Unverified	0
Policy Learning for Malaria Control	Oct 20, 2019	Bayesian OptimizationDecision Making	CodeCode Available	0
Reverse Experience Replay	Oct 19, 2019	Q-Learning	—Unverified	0
Automatic Data Augmentation by Learning the Deterministic Policy	Oct 18, 2019	Data AugmentationDeep Reinforcement Learning	CodeCode Available	0
Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces	Oct 17, 2019	Q-Learningreinforcement-learning	CodeCode Available	0

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