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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 311–320 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Deep Reinforcement Learning for Control of Probabilistic Boolean Networks	Sep 7, 2019	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5
Deep Reinforcement Learning for Multi-class Imbalanced Training	May 24, 2022	Deep Reinforcement Learningimbalanced classification	CodeCode Available	0	5
A Deep Q-Learning Agent for the L-Game with Variable Batch Training	Feb 17, 2018	Q-LearningSelf-Learning	CodeCode Available	0	5
Boosting Soft Q-Learning by Bounding	Jun 26, 2024	Q-Learning	CodeCode Available	0	5
Deep Reinforcement Learning Based Parameter Control in Differential Evolution	May 20, 2019	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5
Bootstrapped Meta-Learning	Sep 9, 2021	Efficient ExplorationFew-Shot Learning	CodeCode Available	0	5
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games	Sep 24, 2020	Q-LearningReinforcement Learning (RL)	CodeCode Available	0	5
Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning	Sep 13, 2023	Q-Learningreinforcement-learning	CodeCode Available	0	5
Deep Reinforcement Learning for Optimal Stopping with Application in Financial Engineering	May 19, 2021	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5
A Machine with Short-Term, Episodic, and Semantic Memory Systems	Dec 5, 2022	Q-LearningReinforcement Learning (RL)	CodeCode Available	0	5

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