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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 1511–1520 of 1918 papers

TitleStatusHype
Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
A deep Q-Learning based Path Planning and Navigation System for Firefighting Environments—0
A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in Smart Grids—0
A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions—0
A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks—0
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise—0
A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents—0
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback—0
A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks—0
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