When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift
arXiv:2603.04648v1 Announce Type: new Abstract: Real-world reinforcement learning systems must operate under distributional drift in their observation streams, yet most policy architectures implicitly assume fully observed and noise-free states. We study robustness of Proximal Policy Optimization (PPO) under temporally persistent sensor failures that induce partial observability and representation shift. To respond to this drift, we augment PPO with temporal sequence models, including Transformers and State Space Models (SSMs), to enable policies to infer missing information from history and maintain performance. Under a stochastic sensor failure process, we prove a high-probability bound on infinite-horizon reward degradation that quantifies how robustness depends on policy smoothness and failure persistence. Empirically, on MuJoCo continuous-control benchmarks with severe sensor dropout, we show Transformer-based sequence policies substantially outperform MLP, RNN, and SSM baselines
arXiv:2603.04648v1 Announce Type: new Abstract: Real-world reinforcement learning systems must operate under distributional drift in their observation streams, yet most policy architectures implicitly assume fully observed and noise-free states. We study robustness of Proximal Policy Optimization (PPO) under temporally persistent sensor failures that induce partial observability and representation shift. To respond to this drift, we augment PPO with temporal sequence models, including Transformers and State Space Models (SSMs), to enable policies to infer missing information from history and maintain performance. Under a stochastic sensor failure process, we prove a high-probability bound on infinite-horizon reward degradation that quantifies how robustness depends on policy smoothness and failure persistence. Empirically, on MuJoCo continuous-control benchmarks with severe sensor dropout, we show Transformer-based sequence policies substantially outperform MLP, RNN, and SSM baselines in robustness, maintaining high returns even when large fractions of sensors are unavailable. These results demonstrate that temporal sequence reasoning provides a principled and practical mechanism for reliable operation under observation drift caused by sensor unreliability.
Executive Summary
The article 'When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift' explores the robustness of Proximal Policy Optimization (PPO) under sensor failures that induce partial observability and representation shift. The authors propose augmenting PPO with temporal sequence models, such as Transformers and State Space Models (SSMs), to enable policies to infer missing information from history. The results demonstrate that Transformer-based sequence policies outperform other baselines in robustness, maintaining high returns even when large fractions of sensors are unavailable. This approach provides a principled mechanism for reliable operation under observation drift caused by sensor unreliability.
Key Points
- ▸ PPO is not robust to sensor failures, which can cause partial observability and representation shift
- ▸ Temporal sequence models, such as Transformers and SSMs, can be used to augment PPO and improve robustness
- ▸ The authors provide a high-probability bound on infinite-horizon reward degradation under stochastic sensor failure processes
Merits
Effective Solution
The proposed approach provides a principled and practical mechanism for reliable operation under observation drift caused by sensor unreliability.
Theoretical Guarantees
The authors provide a high-probability bound on infinite-horizon reward degradation, which quantifies the robustness of the proposed approach.
Demerits
Limited Scope
The article focuses on a specific type of sensor failure and may not be generalizable to other types of failures or more complex environments.
Expert Commentary
The article provides a valuable contribution to the field of reinforcement learning, as it addresses a critical challenge in real-world systems. The proposed approach, which combines PPO with temporal sequence models, is both principled and effective. The theoretical guarantees provided by the authors add to the credibility of the approach. However, further research is needed to extend the scope of the article and explore the applicability of the proposed approach to more complex environments and failure scenarios. The article's findings have significant implications for the development of robust and reliable reinforcement learning systems, and can inform the design of policies and guidelines for safety-critical applications.
Recommendations
- ✓ Future research should explore the applicability of the proposed approach to more complex environments and failure scenarios.
- ✓ The development of more advanced temporal sequence models and their integration with PPO should be investigated to further improve the robustness of reinforcement learning systems.