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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	Score
Adaptive Contention Window Design using Deep Q-learning	Nov 18, 2020	Q-LearningReinforcement Learning (RL)	CodeCode Available	1	5
Deep Recurrent Q-Learning for Partially Observable MDPs	Jul 23, 2015	Atari GamesDeep Reinforcement Learning	CodeCode Available	1	5
Q-learning with Language Model for Edit-based Unsupervised Summarization	Oct 9, 2020	Abstractive Text SummarizationDecoder	CodeCode Available	1	5
QPLEX: Duplex Dueling Multi-Agent Q-Learning	Aug 3, 2020	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	1	5
FACMAC: Factored Multi-Agent Centralised Policy Gradients	Mar 14, 2020	MuJoCoMulti-agent Reinforcement Learning	CodeCode Available	1	5
Deep Active Inference for Partially Observable MDPs	Sep 8, 2020	Deep Reinforcement LearningQ-Learning	CodeCode Available	1	5
Reasoning with Latent Diffusion in Offline Reinforcement Learning	Sep 12, 2023	D4RLOffline RL	CodeCode Available	1	5
Deep Inverse Q-learning with Constraints	Aug 4, 2020	Q-Learning	CodeCode Available	1	5
Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection	Sep 29, 2021	Q-LearningTraffic Signal Control	CodeCode Available	1	5
HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation	May 4, 2021	Bayesian OptimizationQ-Learning	CodeCode Available	1	5

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