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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 1791–1800 of 1918 papers

Title	Date	Tasks	Status	Hype
Multi-Agent Advisor Q-Learning	Oct 26, 2021	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	0
Action Candidate Driven Clipped Double Q-learning for Discrete and Continuous Action Tasks	Mar 22, 2022	Q-Learning	CodeCode Available	0
Playing 2048 With Reinforcement Learning	Oct 20, 2021	Playing the Game of 2048Q-Learning	CodeCode Available	0
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem	Sep 9, 2021	Car RacingQ-Learning	CodeCode Available	0
Deep Reinforcement Learning with a Natural Language Action Space	Nov 14, 2015	Deep Reinforcement LearningQ-Learning	CodeCode Available	0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL	Jul 20, 2024	Few-Shot Text ClassificationQ-Learning	CodeCode Available	0
Decoding fairness: a reinforcement learning perspective	Dec 20, 2024	FairnessImitation Learning	CodeCode Available	0
Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems	Apr 20, 2023	Q-Learningreinforcement-learning	CodeCode Available	0
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks	Aug 1, 2018	Deep Reinforcement LearningQ-Learning	CodeCode Available	0
Traffic Light Control with Reinforcement Learning	Aug 28, 2023	Q-Learningreinforcement-learning	CodeCode Available	0

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