Model tree methods for explaining deep reinforcement learning agents in real-time robotic applications
Deep reinforcement learning has shown useful in the field of robotics but the black-box nature of deep neural networks impedes the applicability of deep reinforcement learning agents for real-world tasks. This is addressed in the field of explainable artificial intelligence, by developing explanatio...
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| Published in: | Neurocomputing (Amsterdam) Vol. 515; pp. 133 - 144 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.01.2023
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | Deep reinforcement learning has shown useful in the field of robotics but the black-box nature of deep neural networks impedes the applicability of deep reinforcement learning agents for real-world tasks. This is addressed in the field of explainable artificial intelligence, by developing explanation methods that aim to explain such agents to humans. Model trees as surrogate models have proven useful for producing explanations for black-box models used in real-world robotic applications, in particular, due to their capability of providing explanations in real time. In this paper, we provide an overview and analysis of available methods for building model trees for explaining deep reinforcement learning agents solving robotics tasks. We find that multiple outputs are important for the model to be able to grasp the dependencies of coupled output features, i.e.actions. Additionally, our results indicate that introducing domain knowledge via a hierarchy among the input features during the building process results in higher accuracies and a faster building process. |
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| AbstractList | Deep reinforcement learning has shown useful in the field of robotics but the black-box nature of deep neural networks impedes the applicability of deep reinforcement learning agents for real-world tasks. This is addressed in the field of explainable artificial intelligence, by developing explanation methods that aim to explain such agents to humans. Model trees as surrogate models have proven useful for producing explanations for black-box models used in real-world robotic applications, in particular, due to their capability of providing explanations in real time. In this paper, we provide an overview and analysis of available methods for building model trees for explaining deep reinforcement learning agents solving robotics tasks. We find that multiple outputs are important for the model to be able to grasp the dependencies of coupled output features, i.e.actions. Additionally, our results indicate that introducing domain knowledge via a hierarchy among the input features during the building process results in higher accuracies and a faster building process. |
| Author | Lekkas, Anastasios M. Strümke, Inga Miller, Timothy Gjærum, Vilde B. Løver, Jakob |
| Author_xml | – sequence: 1 givenname: Vilde B. surname: Gjærum fullname: Gjærum, Vilde B. email: vilde.gjarum@ntnu.no organization: Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7034 Trondheim, Norway – sequence: 2 givenname: Inga surname: Strümke fullname: Strümke, Inga email: inga.strumke@ntnu.no organization: Department of Computer Science, Norwegian University of Science and Technology, 7034 Trondheim, Norway – sequence: 3 givenname: Jakob surname: Løver fullname: Løver, Jakob email: loverjakob@gmail.com organization: Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7052 Trondheim, Norway – sequence: 4 givenname: Timothy surname: Miller fullname: Miller, Timothy email: tmiller@unimelb.edu.au organization: School of Computing and Information Systems, University of Melbourne, 3010 Melbourne, Australia – sequence: 5 givenname: Anastasios M. surname: Lekkas fullname: Lekkas, Anastasios M. email: tmiller@unimelb.edu.au organization: Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7034 Trondheim, Norway |
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| Keywords | Explainable artificial intelligence Model trees Robotics Reinforcement learning |
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| Snippet | Deep reinforcement learning has shown useful in the field of robotics but the black-box nature of deep neural networks impedes the applicability of deep... |
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| SubjectTerms | Explainable artificial intelligence Model trees Reinforcement learning Robotics |
| Title | Model tree methods for explaining deep reinforcement learning agents in real-time robotic applications |
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