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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 1301–1310 of 1918 papers

TitleStatusHype
Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning—0
Self-Imitation Learning via Generalized Lower Bound Q-learning—0
Safety-guaranteed Reinforcement Learning based on Multi-class Support Vector Machine—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Deep Reinforcement Learning for Neural Control—0
Decorrelated Double Q-learning—0
Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework—0
Zeroth-Order Supervised Policy Improvement—0
Q-greedyUCB: a New Exploration Policy for Adaptive and Resource-efficient Scheduling—0
Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning—0
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