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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 1421–1430 of 1918 papers

TitleStatusHype
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization—0
A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint—0
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning—0
Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning—0
Periodic Q-Learning—0
Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks—0
UAV Aided Search and Rescue Operation Using Reinforcement Learning—0
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity—0
Listwise Learning to Rank with Deep Q-Networks—0
Regret Bounds for Discounted MDPs—0
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