Transfer Learning for Multiagent Reinforcement Learning Systems
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3982-3982.
Abstract
Reinforcement learning methods have successfully been applied to build autonomous agents that solve many sequential decision making problems. However, agents need a long time to learn a suitable policy, specially when multiple autonomous agents are in the environment. This research aims to propose a Transfer Learning (TL) framework to accelerate learning by exploiting two knowledge sources: (i) previously learned tasks; and (ii) advising from a more experienced agent. The definition of such framework requires answering several challenging research questions, including: How to abstract and represent knowledge, in order to allow generalization and posterior reuse?, How and when to transfer and receive knowledge in an efficient manner?, and How to evaluate the transfer quality in a Multiagent scenario?.
@inproceedings{ijcai2016p565,
title = {Transfer Learning for Multiagent Reinforcement Learning Systems},
author = {Da Silva, Felipe Leno and Costa, Anna Helena Reali},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {3982--3982},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/565},
}
TY - CPAPER TI - Transfer Learning for Multiagent Reinforcement Learning Systems AU - Da Silva, Felipe Leno AU - Costa, Anna Helena Reali T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 3982 EP - 3982 UR - https://ijcai.org/proceedings/2016/565 ER -Copied
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