التفاصيل البيبلوغرافية
| العنوان: |
Agent Probing Interaction Policies |
| المؤلفون: |
Ghiya, Siddharth, Azeez, Oluwafemi, Miller, Brendan |
| سنة النشر: |
2019 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Multiagent Systems, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: |
Reinforcement learning in a multi agent system is difficult because these systems are inherently non-stationary in nature. In such a case, identifying the type of the opposite agent is crucial and can help us address this non-stationary environment. We have investigated if we can employ some probing policies which help us better identify the type of the other agent in the environment. We've made a simplifying assumption that the other agent has a stationary policy that our probing policy is trying to approximate. Our work extends Environmental Probing Interaction Policy framework to handle multi agent environments. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/1911.09535 |
| رقم الانضمام: |
edsarx.1911.09535 |
| قاعدة البيانات: |
arXiv |