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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 401–410 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Playing Doom with SLAM-Augmented Deep Reinforcement Learning	Dec 1, 2016	Deep Reinforcement Learningobject-detection	CodeCode Available	0	5
Playing FPS Games with Deep Reinforcement Learning	Sep 18, 2016	Deep Reinforcement LearningFPS Games	CodeCode Available	0	5
Control with adaptive Q-learning	Nov 3, 2020	OpenAI GymQ-Learning	CodeCode Available	0	5
Adversarial Learning of a Sampler Based on an Unnormalized Distribution	Jan 3, 2019	FormQ-Learning	CodeCode Available	0	5
Deep Coordination Graphs	Sep 27, 2019	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	0	5
POPO: Pessimistic Offline Policy Optimization	Dec 26, 2020	Offline RLQ-Learning	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
A Comparison of Reward Functions in Q-Learning Applied to a Cart Position Problem	May 25, 2021	PositionQ-Learning	CodeCode Available	0	5
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces	Dec 1, 2019	Privacy PreservingQ-Learning	CodeCode Available	0	5
Deep Ordinal Reinforcement Learning	May 6, 2019	Deep Reinforcement LearningOpenAI Gym	CodeCode Available	0	5

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