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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 431–440 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Automatic Data Augmentation by Learning the Deterministic Policy	Oct 18, 2019	Data AugmentationDeep Reinforcement Learning	CodeCode Available	0	5
A Kernel Loss for Solving the Bellman Equation	May 25, 2019	Q-LearningReinforcement Learning	CodeCode Available	0	5
Automata Learning meets Shielding	Dec 4, 2022	Q-LearningReinforcement Learning (RL)	CodeCode Available	0	5
Adaptive Symmetric Reward Noising for Reinforcement Learning	May 24, 2019	Autonomous DrivingQ-Learning	CodeCode Available	0	5
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings	Oct 29, 2020	Change Point DetectionOff-policy evaluation	CodeCode Available	0	5
SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots	Aug 3, 2021	Model Predictive ControlMotion Planning	CodeCode Available	0	5
CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++	Apr 14, 2018	GPUQ-Learning	CodeCode Available	0	5
Decoding fairness: a reinforcement learning perspective	Dec 20, 2024	FairnessImitation Learning	CodeCode Available	0	5
Scalable Online Exploration via Coverability	Mar 11, 2024	Efficient ExplorationQ-Learning	CodeCode Available	0	5
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem	Sep 9, 2021	Car RacingQ-Learning	CodeCode Available	0	5

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