When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift
📰 ArXiv cs.AI
Temporal sequence models can improve the robustness of Proximal Policy Optimization (PPO) under sensor drift and failures
Action Steps
- Identify potential sensor failure scenarios and their impact on the observation stream
- Augment PPO with temporal sequence models to handle partial observability and representation shift
- Train the model using a robust loss function that accounts for sensor drift and failures
- Evaluate the performance of the robust PPO model under various sensor failure scenarios
Who Needs to Know This
Machine learning researchers and engineers working on reinforcement learning systems can benefit from this research, as it provides a solution to mitigate the effects of sensor drift and failures on PPO performance
Key Insight
💡 Temporal sequence models can effectively handle partial observability and representation shift caused by sensor drift and failures
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🚨 Improve PPO robustness under sensor drift with temporal sequence models! 🚀
Key Takeaways
Temporal sequence models can improve the robustness of Proximal Policy Optimization (PPO) under sensor drift and failures
Full Article
Title: When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift
Abstract:
arXiv:2603.04648v2 Announce Type: replace-cross 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
Abstract:
arXiv:2603.04648v2 Announce Type: replace-cross 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
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