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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 1221–1230 of 1918 papers

TitleStatusHype
Learning to Sketch with Deep Q Networks and Demonstrated Strokes—0
Learning Value Functions from Undirected State-only Experience—0
Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare—0
Lifting the Veil: Unlocking the Power of Depth in Q-learning—0
Linear Q-Learning Does Not Diverge: Convergence Rates to a Bounded Set—0
Listwise Learning to Rank with Deep Q-Networks—0
LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning—0
Location-routing Optimisation for Urban Logistics Using Mobile Parcel Locker Based on Hybrid Q-Learning Algorithm—0
Logical Team Q-learning: An approach towards factored policies in cooperative MARL—0
Logistic Q-Learning—0
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