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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 1371–1380 of 1918 papers

TitleStatusHype
Toward Synergic Learning for Autonomous Manipulation of Deformable Tissues via Surgical Robots: An Approximate Q-Learning Approach—0
Trading the Twitter Sentiment with Reinforcement Learning—0
Traffic Signal Control and Speed Offset Coordination Using Q-Learning for Arterial Road Networks—0
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication—0
Transferred Q-learning—0
Transfer Reinforcement Learning under Unobserved Contextual Information—0
Tuning Path Tracking Controllers for Autonomous Cars Using Reinforcement Learning—0
Two Phase Q-learning for Bidding-based Vehicle Sharing—0
Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach—0
Two-Step Q-Learning—0
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