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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 1861–1870 of 1918 papers

Title	Date	Tasks	Status	Hype
Assumed Density Filtering Q-learning	Dec 9, 2017	Atari GamesBayesian Inference	CodeCode Available	0
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters	Dec 1, 2019	Atari GamesQ-Learning	CodeCode Available	0
Robust Q-Learning for finite ambiguity sets	Jul 5, 2024	Q-Learning	CodeCode Available	0
Cooperation between Independent Market Makers	Jun 11, 2022	Q-Learning	CodeCode Available	0
Robust Q-Learning under Corrupted Rewards	Sep 5, 2024	Q-Learning	CodeCode Available	0
Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks	Feb 10, 2024	Atari GamesDeep Reinforcement Learning	CodeCode Available	0
Active exploration in parameterized reinforcement learning	Oct 6, 2016	Meta-LearningQ-Learning	CodeCode Available	0
Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero	May 28, 2019	Combinatorial OptimizationGraph Neural Network	CodeCode Available	0
Control with adaptive Q-learning	Nov 3, 2020	OpenAI GymQ-Learning	CodeCode Available	0
The Mean-Squared Error of Double Q-Learning	Jul 9, 2020	Q-Learning	CodeCode Available	0

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