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  4. Domain Shifts in Reinforcement Learning: Identifying Disturbances in Environments
 
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2021
Conference Paper
Title

Domain Shifts in Reinforcement Learning: Identifying Disturbances in Environments

Abstract
A significant drawback of End-to-End Deep Reinforcement Learning (RL) systems is that they return an action no matter what situation they are confronted with. This is true even for situations that differ entirely from those an agent has been trained for. Although crucial in safety-critical applications, dealing with such situations is inherently difficult. Various approaches have been proposed in this direction, such as robustness, domain adaption, domain generalization, and out-of-distribution detection. In this work, we provide an overview of approaches towards the more general problem of dealing with disturbances to the environment of RL agents and show how they struggle to provide clear boundaries when mapped to safety-critical problems. To mitigate this, we propose to formalize the changes in the environment in terms of the Markov Decision Process (MDP), resulting in a more formal framework when dealing with such problems. We apply this framework to an example real-world scenario and show how it helps to isolate safety concerns.
Author(s)
Haider, Tom  
Fraunhofer-Institut für Kognitive Systeme IKS  
Schmoeller Roza, Felippe
Fraunhofer-Institut für Kognitive Systeme IKS  
Eilers, Dirk  
Fraunhofer-Institut für Kognitive Systeme IKS  
Roscher, Karsten  
Fraunhofer-Institut für Kognitive Systeme IKS  
Günnemann, Stephan
Technische Univ. München, München
Mainwork
Workshop on Artificial Intelligence Safety, AISafety 2021. Proceedings. Online resource  
Funder
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie StMWi  
Conference
Workshop on Artificial Intelligence Safety (AISafety) 2021  
International Joint Conference on Artificial Intelligence (IJCAI) 2021  
Open Access
DOI
10.24406/publica-fhg-412092
File(s)
N-640441.pdf (3.17 MB)
Rights
CC BY 4.0: Creative Commons Attribution
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Keyword(s)
  • reinforcement learning

  • RL

  • safety critical

  • Markov Decision Process

  • MDP

  • safety

  • Safe Intelligence

  • robustness

  • domain shift

  • out of distribution

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