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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
HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation	May 4, 2021	Bayesian OptimizationQ-Learning	CodeCode Available	1
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient	Oct 13, 2022	Montezuma's RevengeQ-Learning	CodeCode Available	1
Image Classification by Reinforcement Learning with Two-State Q-Learning	Jun 28, 2020	ClassificationGeneral Classification	CodeCode Available	1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?	Sep 26, 2019	Feature EngineeringQ-Learning	CodeCode Available	1
Learning the Markov Decision Process in the Sparse Gaussian Elimination	Sep 30, 2021	Combinatorial OptimizationQ-Learning	CodeCode Available	1
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning	Mar 1, 2023	Continuous ControlImitation Learning	CodeCode Available	1
MAN: Multi-Action Networks Learning	Sep 19, 2022	Atari GamesDeep Reinforcement Learning	CodeCode Available	1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer	Jun 20, 2022	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
An Optimistic Perspective on Offline Deep Reinforcement Learning	Jan 1, 2020	Atari GamesDeep Reinforcement Learning	CodeCode Available	1
A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks	Feb 6, 2020	energy managementenergy trading	CodeCode Available	1

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