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Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models

arXiv:2602.12444v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a powerful framework for optimal decision-making and control but often lacks provable guarantees for safety-critical applications. In this paper, we introduce a novel recovery-based shielding framework that enables safe RL with a provable safety lower bound for unknown and non-linear continuous dynamical systems. The proposed approach integrates a backup policy (shield) with the RL agent, leveraging Gaussian process (GP) based uncertainty quantification to predict potential violations of safety constraints, dynamically recovering to safe trajectories only when necessary. Experience gathered by the 'shielded' agent is used to construct the GP models, with policy optimization via internal model-based sampling - enabling unrestricted exploration and sample efficient learning, without compromising safety. Empirically our approach demonstrates strong performance and strict safety-compliance on a suite of conti

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Alexander W. Goodall, Francesco Belardinelli · · 1 min read · 17 views
#cs.LG #cs.AI

arXiv:2602.12444v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a powerful framework for optimal decision-making and control but often lacks provable guarantees for safety-critical applications. In this paper, we introduce a novel recovery-based shielding framework that enables safe RL with a provable safety lower bound for unknown and non-linear continuous dynamical systems. The proposed approach integrates a backup policy (shield) with the RL agent, leveraging Gaussian process (GP) based uncertainty quantification to predict potential violations of safety constraints, dynamically recovering to safe trajectories only when necessary. Experience gathered by the 'shielded' agent is used to construct the GP models, with policy optimization via internal model-based sampling - enabling unrestricted exploration and sample efficient learning, without compromising safety. Empirically our approach demonstrates strong performance and strict safety-compliance on a suite of continuous control environments.

Executive Summary

The article presents a novel framework for safe reinforcement learning (RL) in unknown and non-linear continuous dynamical systems. The proposed recovery-based shielding method integrates a backup policy with an RL agent, using Gaussian process (GP) based uncertainty quantification to predict and avoid safety violations. This approach allows for unrestricted exploration and sample-efficient learning while ensuring strict safety compliance. Empirical results demonstrate strong performance and safety compliance in various continuous control environments.

Key Points

  • ▸ Introduction of a recovery-based shielding framework for safe RL.
  • ▸ Use of Gaussian process dynamics models for uncertainty quantification.
  • ▸ Integration of a backup policy to ensure safety compliance.
  • ▸ Empirical validation in continuous control environments.

Merits

Provable Safety Guarantees

The framework provides a provable safety lower bound, which is crucial for safety-critical applications.

Sample Efficiency

The approach enables sample-efficient learning through internal model-based sampling, allowing for unrestricted exploration.

Empirical Performance

The method demonstrates strong performance and strict safety compliance in various continuous control environments.

Demerits

Complexity

The integration of GP-based uncertainty quantification and a backup policy adds complexity to the RL framework.

Computational Overhead

The use of GP models and internal model-based sampling may introduce computational overhead, which could be a limitation in resource-constrained environments.

Generalizability

The empirical validation is limited to specific continuous control environments, and the generalizability to other domains remains to be seen.

Expert Commentary

The article presents a significant advancement in the field of safe reinforcement learning by introducing a recovery-based shielding framework that integrates a backup policy with an RL agent. The use of Gaussian process dynamics models for uncertainty quantification is a novel and promising approach, as it allows for the prediction of potential safety violations and dynamic recovery to safe trajectories. The empirical results demonstrate strong performance and strict safety compliance in various continuous control environments, which is a testament to the effectiveness of the proposed method. However, the complexity and computational overhead associated with the integration of GP-based uncertainty quantification and a backup policy may limit its applicability in resource-constrained environments. Additionally, the generalizability of the method to other domains remains to be seen, and further research is needed to validate its effectiveness in a broader range of applications. Overall, the article makes a valuable contribution to the field of safe RL and provides a promising direction for future research.

Recommendations

  • ✓ Further research should focus on reducing the computational overhead and complexity of the proposed framework to make it more applicable in resource-constrained environments.
  • ✓ Empirical validation in a broader range of domains and applications is recommended to assess the generalizability of the method and its effectiveness in different scenarios.

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