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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 1291–1300 of 1918 papers

TitleStatusHype
Multi-Agent Double Deep Q-Learning for Beamforming in mmWave MIMO Networks—0
Multi-Agent Inverse Q-Learning from Demonstrations—0
Multi-Agent Q-Learning Dynamics in Random Networks: Convergence due to Exploration and Sparsity—0
Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids—0
Multi-Agent Q-Learning for Real-Time Load Balancing User Association and Handover in Mobile Networks—0
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks—0
Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs—0
Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing—0
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment—0
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
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