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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 1661–1670 of 1918 papers

Title	Date	Tasks	Status	Hype
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors	Jun 28, 2024	Car RacingQ-Learning	CodeCode Available	0
Self Punishment and Reward Backfill for Deep Q-Learning	Apr 10, 2020	Atari GamesDeep Reinforcement Learning	CodeCode Available	0
Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of Mind	Nov 11, 2024	Q-Learning	CodeCode Available	0
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning	Jul 5, 2021	Atari GamesDeep Reinforcement Learning	CodeCode Available	0
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation	Sep 29, 2017	Deep Reinforcement LearningNavigate	CodeCode Available	0
Double Q-PID algorithm for mobile robot control	Nov 1, 2018	Active LearningQ-Learning	CodeCode Available	0
Adaptive Symmetric Reward Noising for Reinforcement Learning	May 24, 2019	Autonomous DrivingQ-Learning	CodeCode Available	0
Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm	Aug 28, 2023	Deep Reinforcement LearningQ-Learning	CodeCode Available	0
Stochastic approximation with cone-contractive operators: Sharp _-bounds for Q-learning	May 15, 2019	Q-LearningReinforcement Learning	CodeCode Available	0
Distributionally Robust Deep Q-Learning	May 25, 2025	Q-Learning	CodeCode Available	0

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