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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 1701–1710 of 1918 papers

Title	Date	Tasks	Status	Hype
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic	Nov 7, 2016	continuous-controlContinuous Control	CodeCode Available	0
Factors of Influence of the Overestimation Bias of Q-Learning	Oct 11, 2022	Q-Learning	CodeCode Available	0
MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal Control	Dec 20, 2024	Graph AttentionQ-Learning	CodeCode Available	0
ConQUR: Mitigating Delusional Bias in Deep Q-learning	Feb 27, 2020	Atari GamesQ-Learning	CodeCode Available	0
DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data	Oct 8, 2023	Autonomous DrivingQ-Learning	CodeCode Available	0
Making Deep Q-learning methods robust to time discretization	Jan 28, 2019	Deep Reinforcement LearningQ-Learning	CodeCode Available	0
Bootstrapped Meta-Learning	Sep 9, 2021	Efficient ExplorationFew-Shot Learning	CodeCode Available	0
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning	Jul 8, 2021	Hierarchical Reinforcement LearningQ-Learning	CodeCode Available	0
Deep Q-learning from Demonstrations	Apr 12, 2017	Decision MakingDeep Reinforcement Learning	CodeCode Available	0
Reinforcement Learning with Low-Complexity Liquid State Machines	Jun 4, 2019	Atari GamesDeep Reinforcement Learning	CodeCode Available	0

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