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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 1681–1690 of 1918 papers

TitleStatusHype
Accelerated Value Iteration via Anderson Mixing—0
Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective—0
A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning—0
The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals—0
Target Transfer Q-Learning and Its Convergence Analysis—0
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement LearningCode0
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process—0
Optimal Matrix Momentum Stochastic Approximation and Applications to Q-learning—0
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