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A Novel Model for Performance Optimization of Reinforcement Learning (RL) Agent in Stochastic Clinical Scenarios

Principal Investigator
Name
Madeha Arif
Degrees
Ph.D
Institution
National University of Sciences and Technology, Islamabad Pakistan
Position Title
Student
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-837
Initial CDAS Request Approval
Sep 27, 2021
Title
A Novel Model for Performance Optimization of Reinforcement Learning (RL) Agent in Stochastic Clinical Scenarios
Summary
To propose an optimized and generic Deep Reinforcement Learning Model (DRL) that will have dynamic adaptive reward function for stochastic clinical environment and will intelligently cover maximum dynamic behaviors of patients for treating cancer disease.
Aims

In this research we will address:
• Stochastic nature of chronic diseases (handling noisy or incomplete states).
• Credit assignment problem.
• Cost of exploitation and exploration.
• Misleading reward function.

Collaborators

Usman Qamar (National University of sciences and Technology )