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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 1521–1530 of 1918 papers

TitleStatusHype
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
Active inference: demystified and comparedCode0
On the Convergence of Approximate and Regularized Policy Iteration Schemes—0
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach—0
Split Deep Q-Learning for Robust Object Singulation—0
ISL: A novel approach for deep explorationCode0
Joint Inference of Reward Machines and Policies for Reinforcement Learning—0
SQLR: Short-Term Memory Q-Learning for Elastic Provisioning—0
Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders—0
Q-learning Assisted Energy-Aware Traffic Offloading and Cell Switching in Heterogeneous Networks—0
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