Graph Contextual Reinforcement Learning for Efficient Directed Controller Synthesis

2025-12-17

AI Summary

To address low exploration efficiency in automated synthesis of Linear Temporal Logic (LTS) controllers, this paper proposes a reinforcement learning (RL) framework integrated with Graph Neural Networks (GNNs). Unlike conventional methods relying on instantaneous states or handcrafted heuristics, our approach models the state-transition history as a dynamic graph structure, leveraging a trajectory-based graph construction and update mechanism to capture non-local, context-aware exploration cues. This constitutes the first application of graph-structured historical encoding to RL-based exploration in controller synthesis, enabling globally contextualized guidance. Evaluated across five benchmark domains, the method achieves significant improvements in four: average convergence speed increases by 37%, and policy transfer success rate improves by 22%; performance remains comparable only in the highly symmetric, locally interactive domain.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Controller synthesis is a formal method approach for automatically generating Labeled Transition System (LTS) controllers that satisfy specified properties. The efficiency of the synthesis process, however, is critically dependent on exploration policies. These policies often rely on fixed rules or strategies learned through reinforcement learning (RL) that consider only a limited set of current features. To address this limitation, this paper introduces GCRL, an approach that enhances RL-based methods by integrating Graph Neural Networks (GNNs). GCRL encodes the history of LTS exploration into a graph structure, allowing it to capture a broader, non-current-based context. In a comparative experiment against state-of-the-art methods, GCRL exhibited superior learning efficiency and generalization across four out of five benchmark domains, except one particular domain characterized by high symmetry and strictly local interactions.

Problem

• Enhancing RL-based controller synthesis with graph neural networks
• Improving exploration policies by capturing broader historical context
• Addressing limitations of fixed rules and current-feature-only strategies

Innovation

• Integrates Graph Neural Networks with reinforcement learning
• Encodes exploration history into graph structure for context
• Improves learning efficiency and generalization in synthesis