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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 1251–1260 of 1918 papers

TitleStatusHype
MFC-EQ: Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation—0
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning—0
Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning—0
Minimax Optimal Q Learning with Nearest Neighbors—0
Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning—0
Minimizing the Outage Probability in a Markov Decision Process—0
Misspecified Q-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error—0
Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning—0
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning—0
Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning—0
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