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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 1261–1270 of 1918 papers

TitleStatusHype
SlateFree: a Model-Free Decomposition for Reinforcement Learning with Slate Actions—0
Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning—0
Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning—0
Smart Sampling: Self-Attention and Bootstrapping for Improved Ensembled Q-Learning—0
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition—0
Smoothed Action Value Functions for Learning Gaussian Policies—0
Smoothed Q-learning—0
Smooth Q-learning: Accelerate Convergence of Q-learning Using Similarity—0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Soft Q-Learning with Mutual-Information Regularization—0
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