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

Zixuan Yang, Jiaqi Zheng, Guihai Chen

TL;DR

The paper tackles the need for verifiable and interpretable safety in DRL by introducing Reintrainer, a verification-driven interpretation-in-the-loop framework, and Reinfier, a reusable verification/interpreter backend. It combines a Python-embedded language DRLP for property encoding, a Batch Verifier to identify breakpoints, and an interpretation module to answer breakpoint-based questions, with reward shaping guided by a Magnitude and Gap of Property Metric. Through evaluations on six public benchmarks, Reintrainer demonstrates improved reward performance while guaranteeing predefined properties, outperforming the state-of-the-art Trainify and vanilla DRL baselines, and Reinfier provides actionable interpretability insights. The framework promises deployable, property-guaranteed, and interpretable safe DRL by decoupling training from verification and interpretation while enabling flexible property specification and analysis.

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.

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

TL;DR

The paper tackles the need for verifiable and interpretable safety in DRL by introducing Reintrainer, a verification-driven interpretation-in-the-loop framework, and Reinfier, a reusable verification/interpreter backend. It combines a Python-embedded language DRLP for property encoding, a Batch Verifier to identify breakpoints, and an interpretation module to answer breakpoint-based questions, with reward shaping guided by a Magnitude and Gap of Property Metric. Through evaluations on six public benchmarks, Reintrainer demonstrates improved reward performance while guaranteeing predefined properties, outperforming the state-of-the-art Trainify and vanilla DRL baselines, and Reinfier provides actionable interpretability insights. The framework promises deployable, property-guaranteed, and interpretable safe DRL by decoupling training from verification and interpretation while enabling flexible property specification and analysis.

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.

Paper Structure

This paper contains 80 sections, 4 theorems, 32 equations, 6 figures, 15 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Markov Decision Process (MDP).
  4. Formal Verification of DRL Systems.
  5. Framework Overview
  6. Design of Verifier and Interpreter
  7. Descriptive Language: DRLP
  8. Batch Verifier
  9. Interpreter
  10. Sensitivity Analysis
  11. Magnitude and Gap of Property Metric Algorithm
  12. Distance of States
  13. Distance density.
  14. Exact middle point.
  15. Distance of States in $1$-Dimension
  16. ...and 65 more sections

Key Result

Theorem 5.2

Property $\Grave{\phi}\equiv\Grave{P} \Rightarrow \Grave{Q}$ is a relaxation of property $\phi\equiv P\Rightarrow Q$, if $\Grave{P}\subseteq P, \Grave{Q}\supseteq Q$.

Figures (6)

  • Figure 1: Reintrainer architecture.
  • Figure 2: Working procedure of Reinfier.
  • Figure 3: Illustration of four verification scripts characterizing the properties of (a) safety, (b) liveness, (c) local robustness and (d) extreme value robustness.
  • Figure 4: Aurora decision boundary. The red area represents the condition where properties do not hold, while the green area represents the condition where properties hold.
  • Figure 5: Aurora importance analysis results. Each row indicates different original input $\hat{x}_0$ as shown in Table \ref{['tab:imau']}. The first to fourth columns indicate different $N(x_0) \not \approx N(\hat{x_0})$ Levels as shown in Table \ref{['tab:imau']}, and the fifth column indicates the proportion of Importance ($1/Perturbation\ value$) of each feature under the three features of the input.
  • ...and 1 more figures

Theorems & Definitions (10)

  • Definition 5.1
  • Theorem 5.2
  • Definition 1.1
  • Theorem 1.2
  • proof
  • Definition 1.3
  • Theorem 1.4
  • proof
  • Corollary 1.5
  • proof