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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 461–470 of 1918 papers

TitleStatusHype
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities—0
A Tutorial Introduction to Reinforcement Learning—0
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
A Hybrid Q-Learning Sine-Cosine-based Strategy for Addressing the Combinatorial Test Suite Minimization Problem—0
Adaptive Stochastic Resource Control: A Machine Learning Approach—0
A Theory of Regularized Markov Decision Processes—0
A Theoretical Analysis of Deep Q-Learning—0
A Hybrid PAC Reinforcement Learning Algorithm—0
A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning—0
Asynchronous Stochastic Approximation and Average-Reward Reinforcement Learning—0
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