REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning

• URL: http://arxiv.org/abs/2404.03359v1
• Date: Thu, 4 Apr 2024 10:56:30 GMT
• Title: REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning
• Authors: Philipp Altmann, Céline Davignon, Maximilian Zorn, Fabian Ritz, Claudia Linnhoff-Popien, Thomas Gabor,
• Abstract summary: We propose Revealing Evolutionary Action Consequence Trajectories (REACT) to enhance the interpretability of Reinforcement Learning (RL) In contrast to the prevalent practice of RL models based on their optimal behavior learned during training, we posit that considering a range of edge-case trajectories provides a more comprehensive understanding of their inherent behavior. Our results highlight its effectiveness in revealing nuanced aspects of RL models' behavior beyond optimal performance, thereby contributing to improved interpretability.
• Score: 7.889696505137217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT). In contrast to the prevalent practice of validating RL models based on their optimal behavior learned during training, we posit that considering a range of edge-case trajectories provides a more comprehensive understanding of their inherent behavior. To induce such scenarios, we introduce a disturbance to the initial state, optimizing it through an evolutionary algorithm to generate a diverse population of demonstrations. To evaluate the fitness of trajectories, REACT incorporates a joint fitness function that encourages both local and global diversity in the encountered states and chosen actions. Through assessments with policies trained for varying durations in discrete and continuous environments, we demonstrate the descriptive power of REACT. Our results highlight its effectiveness in revealing nuanced aspects of RL models' behavior beyond optimal performance, thereby contributing to improved interpretability.

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