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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 81–90 of 1918 papers

Title	Date	Tasks	Status	Hype
Backprop-Free Reinforcement Learning with Active Neural Generative Coding	Jul 10, 2021	Q-Learningreinforcement-learning	CodeCode Available	1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction	Mar 16, 2020	Deep Reinforcement LearningMeta-Learning	CodeCode Available	1
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning	Sep 13, 2017	Cloud ComputingDeep Reinforcement Learning	CodeCode Available	1
Dropout Q-Functions for Doubly Efficient Reinforcement Learning	Oct 5, 2021	Computational EfficiencyQ-Learning	CodeCode Available	1
Energy-based Surprise Minimization for Multi-Agent Value Factorization	Sep 16, 2020	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning	Jun 7, 2021	Multi-agent Reinforcement LearningOffline RL	CodeCode Available	1
A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning	May 27, 2024	Data AugmentationQ-Learning	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
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning	Oct 30, 2023	Decision MakingOffline RL	CodeCode Available	1
An Optimistic Perspective on Offline Deep Reinforcement Learning	Jan 1, 2020	Atari GamesDeep Reinforcement Learning	CodeCode Available	1

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