# Walker

Published articles for Walker.

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## Dyna-Style Safety Augmented Reinforcement Learning: Staying Safe in the Face of Uncertainty

DevFeed: [Dyna-Style Safety Augmented Reinforcement Learning: Staying Safe in the Face of Uncertainty](<https://devfeed.tech/articles/dyna-style-safety-augmented-reinforcement-learning-staying-safe-in-the-face-of-uncertainty-123123.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2604.25508>)

Author: Artur Eisele, Bernd Frauenknecht, Friedrich Solowjow, Sebastian Trimpe

Published: 2026-11-09T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [ai safety](<https://devfeed.tech/topics/ai-safety.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [control](<https://devfeed.tech/tags/control.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [data](<https://devfeed.tech/tags/data.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [exploration](<https://devfeed.tech/tags/exploration.md>), [filter](<https://devfeed.tech/tags/filter.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [policy](<https://devfeed.tech/tags/policy.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rl](<https://devfeed.tech/tags/rl.md>), [safety](<https://devfeed.tech/tags/safety.md>), [uncertainty](<https://devfeed.tech/tags/uncertainty.md>), [walker](<https://devfeed.tech/tags/walker.md>)

### AI overview

Dyna-SAuR learns both a control policy and a safety filter using an uncertainty-aware dynamics model, with minimal domain knowledge. The filter avoids failures and regions of high model uncertainty; deploying it alongside the policy supports safer data collection and model improvement, allowing conservatism to decrease as the model becomes more informed. The abstract reports two orders of magnitude fewer failures than established safe reinforcement-learning baselines on goal-reaching CartPole and MuJoCo Walker.

### Source excerpt

arXiv:2604.25508v2 Announce Type: replace Abstract: Safety is a key problem in reinforcement learning, especially during exploration. While safety filters are promising to address this issue, they are ill-suited for high- dimensional systems with unknown dynamics. We propose Dyna-style Safety Augmented Reinforcement Learning (Dyna-SAuR), a novel algorithm that learns a control policy as well as a safety filter requiring minimal domain knowledge by leveraging a learned uncertainty-aware dynamics model. A novel filter learning formulation requires the filter to avoid failures and areas of high model uncertainty. As joint deployment of the control policy and filter allows to safely collect data and improve the dynamics model, certain areas grow and filter conservatism decreases over time. Dyna-SAuR reduces failures compared to established safe RL baselines by two orders of magnitude on goal-reaching CartPole and MuJoCo Walker.