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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 1121–1130 of 1918 papers

TitleStatusHype
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better PerformanceCode0
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation—0
Acting in Delayed Environments with Non-Stationary Markov PoliciesCode1
Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems—0
Reinforcement Learning Assisted Beamforming for Inter-cell Interference Mitigation in 5G Massive MIMO Networks—0
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning—0
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach—0
Solving optimal stopping problems with Deep Q-Learning—0
Fire Threat Detection From Videos with Q-Rough Sets—0
Breaking the Deadly Triad with a Target Network—0
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