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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 251–260 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Double Q-PID algorithm for mobile robot control	Nov 1, 2018	Active LearningQ-Learning	CodeCode Available	0	5
Active inference: demystified and compared	Sep 24, 2019	Atari GamesOpenAI Gym	CodeCode Available	0	5
A Multi-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov Games	Jul 5, 2024	Q-Learning	CodeCode Available	0	5
Audio-Driven Reinforcement Learning for Head-Orientation in Naturalistic Environments	Sep 16, 2024	Audio Signal ProcessingDeep Reinforcement Learning	CodeCode Available	0	5
Designing Neural Network Architectures using Reinforcement Learning	Nov 7, 2016	General Classificationimage-classification	CodeCode Available	0	5
DeepTPI: Test Point Insertion with Deep Reinforcement Learning	Jun 7, 2022	Deep Reinforcement LearningGraph Neural Network	CodeCode Available	0	5
A disembodied developmental robotic agent called Samu Bátfai	Nov 9, 2015	Q-LearningReinforcement Learning	CodeCode Available	0	5
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation	Jan 9, 2018	Autonomous DrivingAutonomous Navigation	CodeCode Available	0	5
Deterministic Implementations for Reproducibility in Deep Reinforcement Learning	Sep 15, 2018	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5
Deep Reinforcement Learning with a Natural Language Action Space	Nov 14, 2015	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5

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