analiza aiqbench
This paper proposes RBGA, a method for offline-to-online safe reinforcement learning that tracks the remaining safety budget within each episode and uses it to choose between a pretrained guide policy and the evolving online policy at each step. The core idea is that existing approaches ignore cost already spent, which can starve later exploration. Readers should check the claims against the full experiments, since the abstract gives no quantitative results.
RBGA: Residual-Budget Guide Arbitration for Offline-to-Online Safe Reinforcement Learning in Dexterous Manipulation
arXiv:2610.33494v1 Announce Type: new Abstract: Dexterous manipulation requires robots to leverage existing datasets for rapid skill acquisition while maintaining safety during real-world deployment to prevent damage to the robot or its surroundings. Offline-to-online safe reinforcement learning (O2O safe RL) provides a promising solution for this problem by leveraging prior experience for online improvement under safety constraints, thereby mitigating the sample inefficiency and exploration risk of learning safe policies from scratch. However, existing O2O safe RL approaches do not explicitly account for the safety budget already consumed when making decisions within an episode. Excessive cost incurred early can therefore leave little room for subsequent exploration and policy improvement. To address this limitation, we propose \textbf{R}esidual-\textbf{B}udget \textbf{G}uide \textbf{A}rbitration (RBGA), an algorithm that explicitly tracks the remaining episode safety budget and uses it to adaptively select between offline guidance and the evolving online policy. At each step, RBGA compares the cost-to-go estimates of the guide and online actions against the remaining budget, enabling step-wise action arbitration throughout the episode. We provide theoretical analysis characterizing the safety implications of residual-budget-based selection and its effect on guide-assisted exploration. Extensive experiments demonstrate that RBGA enables effective online policy improvement while maintaining strong safety performance. Our code is available at this \href{https://github.com/sii-pilab}{link}.
Abstract:Dexterous manipulation requires robots to leverage existing datasets for rapid skill acquisition while maintaining safety during real-world deployment to prevent damage to the robot or its surroundings. Offline-to-online safe reinforcement learning (O2O safe RL) provides a promising solution for this problem by leveraging prior experience for online improvement under safety constraints, thereby mitigating the sample inefficiency and exploration risk of learning safe policies from scratch. However, existing O2O safe RL approaches do not explicitly account for the safety budget already consumed when making decisions within an episode. Excessive cost incurred early can therefore leave little room for subsequent exploration and policy improvement. To address this limitation, we propose \textbf{R}esidual-\textbf{B}udget \textbf{G}uide \textbf{A}rbitration (RBGA), an algorithm that explicitly tracks the remaining episode safety budget and uses it to adaptively select between offline guidance and the evolving online policy. At each step, RBGA compares the cost-to-go estimates of the guide and online actions against the remaining budget, enabling step-wise action arbitration throughout the episode. We provide theoretical analysis characterizing the safety implications of residual-budget-based selection and its effect on guide-assisted exploration. Extensive experiments demonstrate that RBGA enables effective online policy improvement while maintaining strong safety performance. Our code is available at this \href{this https URL}{link}.
Machine Learning (cs.LG)
arXiv:2610.33494 [cs.LG]
(or arXiv:2610.33494v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.33494 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 296 cuvinte