تقرير
Collaborative Adaptation: Learning to Recover from Unforeseen Malfunctions in Multi-Robot Teams
| العنوان: | Collaborative Adaptation: Learning to Recover from Unforeseen Malfunctions in Multi-Robot Teams |
|---|---|
| المؤلفون: | Findik, Yasin, Robinette, Paul, Jerath, Kshitij, Ahmadzadeh, S. Reza |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Multiagent Systems |
| الوصف: | Cooperative multi-agent reinforcement learning (MARL) approaches tackle the challenge of finding effective multi-agent cooperation strategies for accomplishing individual or shared objectives in multi-agent teams. In real-world scenarios, however, agents may encounter unforeseen failures due to constraints like battery depletion or mechanical issues. Existing state-of-the-art methods in MARL often recover slowly -- if at all -- from such malfunctions once agents have already converged on a cooperation strategy. To address this gap, we present the Collaborative Adaptation (CA) framework. CA introduces a mechanism that guides collaboration and accelerates adaptation from unforeseen failures by leveraging inter-agent relationships. Our findings demonstrate that CA enables agents to act on the knowledge of inter-agent relations, recovering from unforeseen agent failures and selecting appropriate cooperative strategies. Comment: Presented at Multi-Agent Dynamic Games (MADGames) workshop at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2310.12909 |
| رقم الأكسشن: | edsarx.2310.12909 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |