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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 1591–1600 of 1918 papers

TitleStatusHype
Accelerated Target Updates for Q-learning—0
Comprehensible Context-driven Text Game PlayingCode0
Deep Ordinal Reinforcement LearningCode0
Efficient Model-free Reinforcement Learning in Metric SpacesCode0
Learning agents with prioritization and parameter noise in continuous state and action space—0
Two-Timescale Networks for Nonlinear Value Function Approximation—0
Soft Q-Learning with Mutual-Information Regularization—0
A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks—0
Zap Q-Learning for Optimal Stopping Time Problems—0
Target-Based Temporal Difference Learning—0
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