Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation
This work addresses interpretability and trust issues in AI-driven decision-making for low-dimensional tasks like mobile network resource allocation, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the lack of transparency in deep reinforcement learning for mobile network resource allocation by proposing a method based on Self-Explaining Neural Networks, achieving competitive performance with existing state-of-the-art methods while providing robust explanations.
Reinforcement Learning (RL) methods that incorporate deep neural networks (DNN), though powerful, often lack transparency. Their black-box characteristic hinders interpretability and reduces trustworthiness, particularly in critical domains. To address this challenge in RL tasks, we propose a solution based on Self-Explaining Neural Networks (SENNs) along with explanation extraction methods to enhance interpretability while maintaining predictive accuracy. Our approach targets low-dimensionality problems to generate robust local and global explanations of the model's behaviour. We evaluate the proposed method on the resource allocation problem in mobile networks, demonstrating that SENNs can constitute interpretable solutions with competitive performance. This work highlights the potential of SENNs to improve transparency and trust in AI-driven decision-making for low-dimensional tasks. Our approach strong performance on par with the existing state-of-the-art methods, while providing robust explanations.