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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 1791–1800 of 1918 papers

TitleStatusHype
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes—0
Curriculum Q-Learning for Visual Vocabulary Acquisition—0
A reinforcement learning algorithm for building collaboration in multi-agent systems—0
Classification with Costly Features using Deep Reinforcement LearningCode0
Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
A unified decision making framework for supply and demand management in microgrid networks—0
Double Q(σ) and Q(σ, λ): Unifying Reinforcement Learning Control Algorithms—0
The Effects of Memory Replay in Reinforcement LearningCode0
Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations—0
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