Reinfier and Reintrainer: Verification and Interpretation-Driven Safe Deep Reinforcement Learning Frameworks

• URL: http://arxiv.org/abs/2410.15127v2
• Date: Tue, 04 Feb 2025 14:01:02 GMT
• Title: Reinfier and Reintrainer: Verification and Interpretation-Driven Safe Deep Reinforcement Learning Frameworks
• Authors: Zixuan Yang, Jiaqi Zheng, Guihai Chen,
• Abstract summary: We propose a verification-driven interpretation-in-the-loop framework Reintrainer to develop trustworthy DRL models.<n>In each iteration, this framework measures the gap between the on-training model and predefined properties using formal verification.<n>Reinfier features breakpoints searching and verification-driven interpretation, associated with a concise constraint-encoding language DRLP.
• Score: 36.730973051834376
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring verifiable and interpretable safety of deep reinforcement learning (DRL) is crucial for its deployment in real-world applications. Existing approaches like verification-in-the-loop training, however, face challenges such as difficulty in deployment, inefficient training, lack of interpretability, and suboptimal performance in property satisfaction and reward performance. In this work, we propose a novel verification-driven interpretation-in-the-loop framework Reintrainer to develop trustworthy DRL models, which are guaranteed to meet the expected constraint properties. Specifically, in each iteration, this framework measures the gap between the on-training model and predefined properties using formal verification, interprets the contribution of each input feature to the model's output, and then generates the training strategy derived from the on-the-fly measure results, until all predefined properties are proven. Additionally, the low reusability of existing verifiers and interpreters motivates us to develop Reinfier, a general and fundamental tool within Reintrainer for DRL verification and interpretation. Reinfier features breakpoints searching and verification-driven interpretation, associated with a concise constraint-encoding language DRLP. Evaluations demonstrate that Reintrainer outperforms the state-of-the-art on six public benchmarks in both performance and property guarantees. Our framework can be accessed at https://github.com/Kurayuri/Reinfier.

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