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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 91–100 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning	Mar 1, 2023	Continuous ControlImitation Learning	CodeCode Available	1	5
MADiff: Offline Multi-agent Learning with Diffusion Models	May 27, 2023	Offline RLQ-Learning	CodeCode Available	1	5
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Past	Jun 10, 2019	Deep Reinforcement LearningMuJoCo	CodeCode Available	1	5
Boosting Continuous Control with Consistency Policy	Oct 10, 2023	continuous-controlContinuous Control	CodeCode Available	1	5
Deep Recurrent Q-Learning for Partially Observable MDPs	Jul 23, 2015	Atari GamesDeep Reinforcement Learning	CodeCode Available	1	5
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?	Dec 1, 2020	Feature EngineeringQ-Learning	CodeCode Available	1	5
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning	May 2, 2022	Data AugmentationQ-Learning	CodeCode Available	1	5
ModelicaGym: Applying Reinforcement Learning to Modelica Models	Sep 18, 2019	Q-Learningreinforcement-learning	CodeCode Available	1	5
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning	Mar 9, 2023	Offline RLQ-Learning	CodeCode Available	1	5
A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks	Feb 6, 2020	energy managementenergy trading	CodeCode Available	1	5

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