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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 1901–1910 of 1918 papers

Title	Date	Tasks	Status	Hype
Deep Reinforcement Learning for Control of Probabilistic Boolean Networks	Sep 7, 2019	Deep Reinforcement LearningQ-Learning	CodeCode Available	0
Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model	Oct 27, 2024	D4RLQ-Learning	CodeCode Available	0
Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure Games	Aug 13, 2019	Common Sense ReasoningQ-Learning	CodeCode Available	0
The Impact of Data Distribution on Q-learning with Function Approximation	Nov 23, 2021	DiversityQ-Learning	CodeCode Available	0
Sampled Policy Gradient for Learning to Play the Game Agar.io	Sep 15, 2018	Game DesignQ-Learning	CodeCode Available	0
Learning an Interpretable Traffic Signal Control Policy	Dec 23, 2019	Q-LearningReinforcement Learning	CodeCode Available	0
Control of nonlinear, complex and black-boxed greenhouse system with reinforcement learning	Jul 30, 2019	Q-Learningreinforcement-learning	CodeCode Available	0
Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement Learning	Dec 21, 2022	Deep Reinforcement LearningQ-Learning	CodeCode Available	0
Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time Systems	Jan 13, 2021	Q-LearningReinforcement Learning (RL)	CodeCode Available	0
Continuous Control With Ensemble Deep Deterministic Policy Gradients	Nov 30, 2021	continuous-controlContinuous Control	CodeCode Available	0

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