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Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policies

Topics: Autonomous Systems; Deep Learning; Explainable Artificial Intelligence; Fairness and Reliability; Interpretable Artificial Intelligence; Machine Learning; Planning and Scheduling; Privacy, Safety, Security, and Ethical Issues; Task Planning and Execution; Transparency; Uncertainty in AI

Authors: Dennis Gross and Helge Spieker

Affiliation: Simula Research Laboratory, Norway

Keyword(s): Explainable Reinforcement Learning, Model Checking, Co-Activation Graph Analysis.

Abstract: Deep reinforcement learning (RL) policies can demonstrate unsafe behaviors and are challenging to interpret. To address these challenges, we combine RL policy model checking—a technique for determining whether RL policies exhibit unsafe behaviors—with co-activation graph analysis—a method that maps neural network inner workings by analyzing neuron activation patterns—to gain insight into the safe RL policy’s sequential decision-making. This combination lets us interpret the RL policy’s inner workings for safe decision-making. We demonstrate its applicability in various experiments.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Gross, D. and Spieker, H. (2025). Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policies. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5; ISSN 2184-433X, SciTePress, pages 611-621. DOI: 10.5220/0013255500003890

@conference{icaart25,
author={Dennis Gross and Helge Spieker},
title={Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policies},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={611-621},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013255500003890},
isbn={978-989-758-737-5},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policies
SN - 978-989-758-737-5
IS - 2184-433X
AU - Gross, D.
AU - Spieker, H.
PY - 2025
SP - 611
EP - 621
DO - 10.5220/0013255500003890
PB - SciTePress

- Science and Technology Publications, Lda.
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