Title

Enhancing Supervisory Training Signals with Environmental Reinforcement Learning Using Adaptive Dynamic Programming

Presenter Information

Niklas M. Melton

Department

Mechanical and Aerospace Engineering

Major

Aerospace Engineering

Research Advisor

Wunsch, Donald C.

Advisor's Department

Electrical and Computer Engineering

Abstract

A method for hybridizing supervised learning with adaptive dynamic programming was developed to increase the speed, quality, and robustness of on-line neural network learning from an imperfect teacher. Reinforcement learning is used to modify and enhance the original supervisory signal before learning occurs. This research describes the method of hybridization and presents a model problem in which a human supervisor teaches a simulated car to drive around a race track. Simulation results show successful learning and improvements in convergence time, error rate, and stability over both component methods alone.

Biography

Niklas Melton is a senior aerospace engineering major from Kansas City, MO. Since coming to MS&T, he has continued to develop and refine his technical interests and skills with a focus on biologically inspired technologies. He is a Student Council representative for the iGEM student design team and is an active member of the Applied Computational Intelligence Lab, where he researches controls applications of neural networks.

Research Category

Engineering

Presentation Type

Oral Presentation

Document Type

Presentation

Award

Engineering oral presentation, First place

Location

Turner Room

Presentation Date

11 Apr 2016, 10:40 am - 11:00 am

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Apr 11th, 10:40 AM Apr 11th, 11:00 AM

Enhancing Supervisory Training Signals with Environmental Reinforcement Learning Using Adaptive Dynamic Programming

Turner Room

A method for hybridizing supervised learning with adaptive dynamic programming was developed to increase the speed, quality, and robustness of on-line neural network learning from an imperfect teacher. Reinforcement learning is used to modify and enhance the original supervisory signal before learning occurs. This research describes the method of hybridization and presents a model problem in which a human supervisor teaches a simulated car to drive around a race track. Simulation results show successful learning and improvements in convergence time, error rate, and stability over both component methods alone.