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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 931–940 of 1918 papers

TitleStatusHype
Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Driving—0
Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning—0
Orchestrated Value Mapping for Reinforcement LearningCode0
Reinforcement Learning for Optimal Control of a District Cooling Energy Plant—0
The Efficacy of Pessimism in Asynchronous Q-Learning—0
A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets—0
Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery—0
Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility—0
Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit—0
Target Network and Truncation Overcome The Deadly Triad in Q-Learning—0
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