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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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1721–1730 of 1918 papers

Title	Date	Tasks	Status	Hype
Remember and Forget for Experience Replay	Jul 16, 2018	Deep Reinforcement LearningPolicy Gradient Methods	CodeCode Available	0
Video Summarisation by Classification with Deep Reinforcement Learning	Jul 9, 2018	ClassificationDecision Making	—Unverified	0
Playing against Nature: causal discovery for decision making under uncertainty	Jul 3, 2018	Causal DiscoveryDecision Making	—Unverified	0
Learning to Explore via Meta-Policy Gradient	Jul 1, 2018	continuous-controlContinuous Control	—Unverified	0
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning	Jul 1, 2018	Hierarchical Reinforcement LearningQ-Learning	CodeCode Available	0
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems	Jul 1, 2018	Multi-Armed BanditsQ-Learning	—Unverified	0
Many-Goals Reinforcement Learning	Jun 22, 2018	AllQ-Learning	—Unverified	0
Reinforcement Learning using Augmented Neural Networks	Jun 20, 2018	Q-Learningreinforcement-learning	—Unverified	0
Action Learning for 3D Point Cloud Based Organ Segmentation	Jun 14, 2018	Organ SegmentationQ-Learning	—Unverified	0
Automatic formation of the structure of abstract machines in hierarchical reinforcement learning with state clustering	Jun 13, 2018	ClusteringHierarchical Reinforcement Learning	—Unverified	0

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