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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 1481–1490 of 1918 papers

Title	Date	Tasks	Status	Hype
Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach	Nov 8, 2019	Q-Learning	—Unverified	0
Challenging On Car Racing Problem from OpenAI gym	Nov 2, 2019	Car Racingcontinuous-control	—Unverified	0
On Solving the 2-Dimensional Greedy Shooter Problem for UAVs	Nov 2, 2019	Q-Learningreinforcement-learning	CodeCode Available	0
Generalized Speedy Q-learning	Nov 1, 2019	Q-LearningReinforcement Learning	CodeCode Available	0
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning	Oct 28, 2019	General Reinforcement LearningQ-Learning	—Unverified	0
Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning	Oct 28, 2019	Q-Learningreinforcement-learning	—Unverified	0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning	Oct 27, 2019	Deep Reinforcement LearningImitation Learning	CodeCode Available	0
D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments	Oct 26, 2019	PositionQ-Learning	—Unverified	0
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations	Oct 26, 2019	Atari GamesDeep Reinforcement Learning	CodeCode Available	0
Deep Q-Learning for Same-Day Delivery with Vehicles and Drones	Oct 25, 2019	Decision MakingQ-Learning	—Unverified	0

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