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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 1801–1810 of 1918 papers

TitleStatusHype
Supervised Q-walk for Learning Vector Representation of Nodes in Networks—0
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot NavigationCode0
A Simple Reinforcement Learning Mechanism for Resource Allocation in LTE-A Networks with Markov Decision Process and Q-Learning—0
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning—0
Improving Search through A3C Reinforcement Learning based Conversational Agent—0
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning—0
Constructing narrative using a generative model and continuous action policies—0
Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids—0
Practical Block-wise Neural Network Architecture GenerationCode0
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