Teachability And Interpretability In Reinforcement Learning

Authors

Jeevan Rajagopal, University of Nebraska - LincolnFollow

First Advisor

Stephen D. Scott

Date of this Version

Spring 5-2021

Citation

@article{TIDQN,author={Jeevan Rajagopal},title={Teachability and Interpretability in Reinforcement Learning}, year = {2021}}

Comments

The most recent version of this work can be found at https://github.com/blackhole077/TIDQN-repository

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Computer Science, Under the Supervision of Professor Stephen Donald Scott. Lincoln, Nebraska: May, 2021

Copyright 2021 Jeevan Rajagopal

Abstract

There have been many recent advancements in the field of reinforcement learning, starting from the Deep Q Network playing various Atari 2600 games all the way to Google Deempind's Alphastar playing competitively in the game StarCraft. However, as the field challenges more complex environments, the current methods of training models and understanding their decision making become less effective. Currently, the problem is partially dealt with by simply adding more resources, but the need for a better solution remains.

This thesis proposes a reinforcement learning framework where a teacher or entity with domain knowledge of the task to complete can assist the agent in finding a policy, while also providing human observers a more understandable format for how the agent formulates its overall policy to complete a given task. This framework is evaluated on the Seaquest environment within the OpenAI Gym framework.

Adviser: Stephen Scott