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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 111–120 of 1918 papers

Title	Date	Tasks	Status	Hype
Adaptive Contention Window Design using Deep Q-learning	Nov 18, 2020	Q-LearningReinforcement Learning (RL)	CodeCode Available	1
Playing Atari with Deep Reinforcement Learning	Dec 19, 2013	Atari GamesDeep Reinforcement Learning	CodeCode Available	1
Benchmarking Batch Deep Reinforcement Learning Algorithms	Oct 3, 2019	BenchmarkingDeep Reinforcement Learning	CodeCode Available	1
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials	Oct 11, 2022	Offline RLQ-Learning	CodeCode Available	1
QPLEX: Duplex Dueling Multi-Agent Q-Learning	Aug 3, 2020	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	1
Towards Universal and Black-Box Query-Response Only Attack on LLMs with QROA	Jun 4, 2024	Q-Learning	CodeCode Available	1
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning	Jul 12, 2022	Lifelong learningPolicy Gradient Methods	CodeCode Available	1
Reasoning with Latent Diffusion in Offline Reinforcement Learning	Sep 12, 2023	D4RLOffline RL	CodeCode Available	1
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation	Jun 23, 2021	Continuous ControlQ-Learning	CodeCode Available	1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning	Feb 16, 2021	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1

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