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Hybrid knowledge transfer for MARL based on action advising and experience sharing
- Source :
- Frontiers in Neurorobotics, Vol 18 (2024)
- Publication Year :
- 2024
- Publisher :
- Frontiers Media S.A., 2024.
-
Abstract
- Multiagent Reinforcement Learning (MARL) has been well adopted due to its exceptional ability to solve multiagent decision-making problems. To further enhance learning efficiency, knowledge transfer algorithms have been developed, among which experience-sharing-based and action-advising-based transfer strategies share the mainstream. However, it is notable that, although there exist many successful applications of both strategies, they are not flawless. For the long-developed action-advising-based methods (namely KT-AA, short for knowledge transfer based on action advising), their data efficiency and scalability are not satisfactory. As for the newly proposed experience-sharing-based knowledge transfer methods (KT-ES), although the shortcomings of KT-AA have been partially overcome, they are incompetent to correct specific bad decisions in the later learning stage. To leverage the superiority of both KT-AA and KT-ES, this study proposes KT-Hybrid, a hybrid knowledge transfer approach. In the early learning phase, KT-ES methods are employed, expecting better data efficiency from KT-ES to enhance the policy to a basic level as soon as possible. Later, we focus on correcting specific errors made by the basic policy, trying to use KT-AA methods to further improve the performance. Simulations demonstrate that the proposed KT-Hybrid outperforms well-received action-advising- and experience-sharing-based methods.
Details
- Language :
- English
- ISSN :
- 16625218
- Volume :
- 18
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Neurorobotics
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.360e1ded9e834f76a2a3ec3f3cdfec23
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fnbot.2024.1364587