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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 51–60 of 1918 papers

Title	Date	Tasks	Status	Hype
Distilling Reinforcement Learning Tricks for Video Games	Jul 1, 2021	Q-Learningreinforcement-learning	CodeCode Available	1
Distributed Heuristic Multi-Agent Path Finding with Communication	Jun 21, 2021	Multi-Agent Path FindingQ-Learning	CodeCode Available	1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning	Mar 9, 2023	Offline RLQ-Learning	CodeCode Available	1
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning	Sep 13, 2017	Cloud ComputingDeep Reinforcement Learning	CodeCode Available	1
Adaptive Contention Window Design using Deep Q-learning	Nov 18, 2020	Q-LearningReinforcement Learning (RL)	CodeCode Available	1
Addressing Function Approximation Error in Actor-Critic Methods	Feb 26, 2018	Continuous ControlOpenAI Gym	CodeCode Available	1
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques	Mar 21, 2020	Q-Learningreinforcement-learning	CodeCode Available	1
A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks	Feb 6, 2020	energy managementenergy trading	CodeCode Available	1
Gradient Temporal-Difference Learning with Regularized Corrections	Jul 1, 2020	Q-Learning	CodeCode Available	1
Backprop-Free Reinforcement Learning with Active Neural Generative Coding	Jul 10, 2021	Q-Learningreinforcement-learning	CodeCode Available	1

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