UNDERSTANDING THE ADVERSE EFFECTS OF ACCELERATING REINFORCEMENT LEARNING WITH HUMAN TRAINERS
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Authors
Hee, Brandon R.
Subjects
reinforcement learning
artificial intelligence
human trainer
Atari
artificial intelligence
human trainer
Atari
Advisors
Xie, Geoffrey G.
Date of Issue
2020-09
Date
Sep-20
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Recent advances in reinforcement learning (RL) have propelled the idea that artificially intelligent agents may one day replace humans in performing complex tasks. There are numerous challenges associated with moving RL from a simulated environment to the real world. In particular, understanding the decision-making process of the RL agents and ascertaining the viability of use in safety-constrained environments are key challenges. An evolving approach to addressing these challenges is to impart human knowledge into the learning algorithms. Through a comprehensive evaluation using a Pong RL agent, this thesis provides evidence that incorporating human influence into an RL algorithm can cause a strategy conflict and impede learning. In particular, it shows that (i) there is an inflection point measured by training episodes with respect to the positive effect of incorporating human influence for the Pong agent and that (ii) if human influence is not decayed beyond the inflection point, the negative effect can intensify and eventually undo all prior training gains.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
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NPS Report Number
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Distribution Statement
Approved for public release. distribution is unlimited
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States