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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 181–190 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based Services	Dec 6, 2024	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement	Oct 22, 2018	Policy Gradient MethodsQ-Learning	CodeCode Available	0	5
Double Q-PID algorithm for mobile robot control	Nov 1, 2018	Active LearningQ-Learning	CodeCode Available	0	5
Distributionally Robust Deep Q-Learning	May 25, 2025	Q-Learning	CodeCode Available	0	5
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning	Sep 10, 2024	Deep Reinforcement LearningOpenAI Gym	CodeCode Available	0	5
Dual Ensembled Multiagent Q-Learning with Hypernet Regularizer	Feb 4, 2025	Q-LearningSMAC	CodeCode Available	0	5
Efficient Sparse-Reward Goal-Conditioned Reinforcement Learning with a High Replay Ratio and Regularization	Dec 10, 2023	Q-LearningReinforcement Learning (RL)	CodeCode Available	0	5
A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services	Mar 23, 2024	FairnessQ-Learning	CodeCode Available	0	5
Adversarial Learning of a Sampler Based on an Unnormalized Distribution	Jan 3, 2019	FormQ-Learning	CodeCode Available	0	5
Diagnosing Bottlenecks in Deep Q-learning Algorithms	Feb 26, 2019	continuous-controlContinuous Control	CodeCode Available	0	5

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