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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 751–760 of 1918 papers

TitleStatusHype
Entropy-Augmented Entropy-Regularized Reinforcement Learning and a Continuous Path from Policy Gradient to Q-Learning—0
Fidelity-based Probabilistic Q-learning for Control of Quantum Systems—0
Final Adaptation Reinforcement Learning for N-Player Games—0
Finding the best design parameters for optical nanostructures using reinforcement learning—0
Finite Horizon Q-learning: Stability, Convergence, Simulations and an application on Smart Grids—0
Finite-Sample Analysis for SARSA with Linear Function Approximation—0
Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model—0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Chemoreception and chemotaxis of a three-sphere swimmer—0
Ensemble Bootstrapping for Q-Learning—0
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