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

• URL: http://arxiv.org/abs/2501.03142v1
• Date: Mon, 06 Jan 2025 17:07:44 GMT
• Title: Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policies
• Authors: Dennis Gross, Helge Spieker,
• Abstract summary: Deep reinforcement learning (RL) policies can demonstrate unsafe behaviors and are challenging to interpret. We combine RL policy model checking and co-activation graph analysis. This combination lets us interpret the RL policy's inner workings for safe decision-making.
• Score: 5.923818043882103
• License:
• 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.

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