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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 1371–1380 of 1918 papers

TitleStatusHype
Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation—0
Entropy-Augmented Entropy-Regularized Reinforcement Learning and a Continuous Path from Policy Gradient to Q-Learning—0
A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions—0
A Deep Reinforcement Learning Approach to Efficient Drone Mobility Support—0
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning—0
Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python—0
Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach—0
Implementing Inductive bias for different navigation tasks through diverse RNN attrractors—0
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD—0
Whittle index based Q-learning for restless bandits with average reward—0
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