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Robust Exploration in Directed Controller Synthesis via Reinforcement Learning with Soft Mixture-of-Experts

arXiv:2602.19244v1 Announce Type: new Abstract: On-the-fly Directed Controller Synthesis (OTF-DCS) mitigates state-space explosion by incrementally exploring the system and relies critically on an exploration policy to guide search efficiently. Recent reinforcement learning (RL) approaches learn such policies and achieve promising zero-shot generalization from small training instances to larger unseen ones. However, a fundamental limitation is anisotropic generalization, where an RL policy exhibits strong performance only in a specific region of the domain-parameter space while remaining fragile elsewhere due to training stochasticity and trajectory-dependent bias. To address this, we propose a Soft Mixture-of-Experts framework that combines multiple RL experts via a prior-confidence gating mechanism and treats these anisotropic behaviors as complementary specializations. The evaluation on the Air Traffic benchmark shows that Soft-MoE substantially expands the solvable parameter space

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Toshihide Ubukata, Zhiyao Wang, Enhong Mu, Jialong Li, Kenji Tei · · 1 min read · 2 views
#cs.AI #cs.LG

arXiv:2602.19244v1 Announce Type: new Abstract: On-the-fly Directed Controller Synthesis (OTF-DCS) mitigates state-space explosion by incrementally exploring the system and relies critically on an exploration policy to guide search efficiently. Recent reinforcement learning (RL) approaches learn such policies and achieve promising zero-shot generalization from small training instances to larger unseen ones. However, a fundamental limitation is anisotropic generalization, where an RL policy exhibits strong performance only in a specific region of the domain-parameter space while remaining fragile elsewhere due to training stochasticity and trajectory-dependent bias. To address this, we propose a Soft Mixture-of-Experts framework that combines multiple RL experts via a prior-confidence gating mechanism and treats these anisotropic behaviors as complementary specializations. The evaluation on the Air Traffic benchmark shows that Soft-MoE substantially expands the solvable parameter space and improves robustness compared to any single expert.

Executive Summary

This article proposes a novel approach to robust exploration in directed controller synthesis via reinforcement learning with a Soft Mixture-of-Experts framework. The framework addresses the limitation of anisotropic generalization by combining multiple RL experts, thereby expanding the solvable parameter space and improving robustness. The evaluation on the Air Traffic benchmark demonstrates the effectiveness of the proposed approach. The article contributes to the development of more efficient and scalable directed controller synthesis methods, with potential applications in various domains. The proposed framework has the potential to mitigate the state-space explosion problem and improve the overall performance of the system.

Key Points

  • ▸ Introduction of a Soft Mixture-of-Experts framework for robust exploration in directed controller synthesis
  • ▸ Addressing the limitation of anisotropic generalization in reinforcement learning
  • ▸ Evaluation on the Air Traffic benchmark demonstrating improved robustness and expanded solvable parameter space

Merits

Improved Robustness

The proposed Soft Mixture-of-Experts framework improves the robustness of directed controller synthesis by combining multiple RL experts and mitigating anisotropic generalization.

Expanded Solvable Parameter Space

The framework expands the solvable parameter space, allowing for more efficient and scalable directed controller synthesis.

Demerits

Complexity of the Framework

The proposed framework may introduce additional complexity, requiring careful tuning of hyperparameters and expert selection.

Expert Commentary

The proposed Soft Mixture-of-Experts framework represents a significant advancement in addressing the limitations of anisotropic generalization in reinforcement learning. By combining multiple RL experts, the framework improves the robustness and expands the solvable parameter space of directed controller synthesis. The evaluation on the Air Traffic benchmark demonstrates the effectiveness of the proposed approach. However, the complexity of the framework may require careful consideration and tuning of hyperparameters. Overall, the article contributes to the development of more efficient and scalable directed controller synthesis methods, with potential applications in various domains.

Recommendations

  • ✓ Further evaluation of the proposed framework on additional benchmarks and domains
  • ✓ Investigation of the potential applications of the framework in real-world systems and scenarios

Sources

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