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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 731–740 of 1918 papers

TitleStatusHype
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations—0
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective—0
Revisiting Discrete Soft Actor-CriticCode1
MAN: Multi-Action Networks LearningCode1
Comparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents—0
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning—0
M^2DQN: A Robust Method for Accelerating Deep Q-learning NetworkCode0
Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources—0
IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture—0
Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks—0
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