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Control with Distributed Deep Reinforcement Learning: Learn a Better Policy
Distributed approach is a very effective method to improve training effi...
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EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection
Epistasis (gene-gene interaction) is crucial to predicting genetic disea...
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TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning
We propose a novel approach to interactive theorem-proving (ITP) using d...
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Learning medical triage from clinicians using Deep Q-Learning
Medical Triage is of paramount importance to healthcare systems, allowin...
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Learning Reinforced Agents with Counterfactual Simulation for Medical Automatic Diagnosis
Medical automatic diagnosis (MAD) aims to learn an agent that mimics the...
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Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning
Existing automatic 3D image segmentation methods usually fail to meet th...
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Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation
Mild cognitive impairment (MCI) is a prodromal phase in the progression ...
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Effective Medical Test Suggestions Using Deep Reinforcement Learning
Effective medical test suggestions benefit both patients and physicians to conserve time and improve diagnosis accuracy. In this work, we show that an agent can learn to suggest effective medical tests. We formulate the problem as a stage-wise Markov decision process and propose a reinforcement learning method to train the agent. We introduce a new representation of multiple action policy along with the training method of the proposed representation. Furthermore, a new exploration scheme is proposed to accelerate the learning of disease distributions. Our experimental results demonstrate that the accuracy of disease diagnosis can be significantly improved with good medical test suggestions.
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