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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
Double Q-PID algorithm for mobile robot controlCode0
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
Finding the best design parameters for optical nanostructures using reinforcement learning—0
Assessing the Potential of Classical Q-learning in General Game PlayingCode0
Learning to Sketch with Deep Q Networks and Demonstrated Strokes—0
Learning to Reason—0
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