Understanding the Behavior of Reinforcement Learning Agents

Jörg Stork*, Martin Zaefferer, Thomas Bartz-Beielstein, A. E. Eiben

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Abstract

Reinforcement Learning (RL) is the process of training agents to solve specific tasks, based on measures of reward. Understanding the behavior of an agent in its environment can be crucial. For instance, if users understand why specific agents fail at a task, they might be able to define better reward functions, to steer the agents’ development in the right direction. Understandability also empowers decisions for agent deployment. If we know why the controller of an autonomous car fails or excels in specific traffic situations, we can make better decisions on whether/when to use them in practice. We aim to facilitate the understandability of RL. To that end, we investigate and observe the behavioral space: the set of actions of an agent observed for a set of input states. Consecutively, we develop measures of distance or similarity in that space and analyze how agents compare in their behavior. Moreover, we investigate which states and actions are critical for a task, and determine the correlation between reward and behavior. We utilize two basic RL environments to investigate our measures. The results showcase the high potential of inspecting an agents’ behavior and comparing their distance in behavior space.

Original languageEnglish
Title of host publicationBioinspired Optimization Methods and Their Applications
Subtitle of host publication9th International Conference, BIOMA 2020 Brussels, Belgium, November 19–20, 2020 Proceedings
EditorsBogdan Filipic, Edmondo Minisci, Massimiliano Vasile
PublisherSpringer Science and Business Media Deutschland GmbH
Pages148-160
Number of pages13
ISBN (Electronic)9783030637101
ISBN (Print)9783030637095
DOIs
Publication statusPublished - 2020
Event9th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2020 - Brussels, Belgium
Duration: 19 Nov 202020 Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12438 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2020
Country/TerritoryBelgium
CityBrussels
Period19/11/2020/11/20

Keywords

  • Behavior
  • Reinforcement Learning
  • Understandable AI

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