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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 281–290 of 1918 papers

TitleStatusHype
A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
Accelerated Value Iteration via Anderson Mixing—0
A Note on Target Q-learning For Solving Finite MDPs with A Generative Oracle—0
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems—0
A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint—0
Accelerated Target Updates for Q-learning—0
Bridging the Gap Between Value and Policy Based Reinforcement Learning—0
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning—0
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms—0
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