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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 1661–1670 of 1918 papers

TitleStatusHype
Deep Reinforcement Learning for Green Security Games with Real-Time Information—0
Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting—0
Double Q-PID algorithm for mobile robot controlCode0
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning—0
Structure Learning of Deep Neural Networks with Q-Learning—0
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach—0
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks—0
Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning—0
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy ImprovementCode0
Optimization of Molecules via Deep Reinforcement LearningCode1
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