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A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems
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Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem, providing a comprehensive discussion of recent progress on knowledge reuse in Multiagent Systems (MAS) and of techniques for knowledge reuse across agents (that may be actuating in a shared environment or not). We aim at encouraging the community to work towards reusing all the knowledge sources available in a MAS. For that, we provide an in-depth discussion of current lines of research and open questions.
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A Review on Simulation Platforms for Agent-Based Modeling in Electrified Transportation
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Self-citationUPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers
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Metadata
- Authors
- Silva, F. L. D., & Costa, A. H. R.
- Affiliations
- Universidade de São Paulo (BR)
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2019-03-11
- Volume
- 64
- Pages
- 645-703
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2921955147
- Cited by
- 273
- References
- 170
- Citations by year
- 2026: 18 · 2025: 33 · 2024: 38 · 2023: 43 · 2022: 40 · 2021: 56 · 2020: 34 · 2019: 10
- FWCI
- 15.97
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Artificial Intelligence Research (publishedVersion, cc-by)
- Funders
- University of Texas at Austin
- Fundação de Amparo à Pesquisa do Estado de São Paulo (16/21047-3, 2015/, 2015/16310-4, 2018/00344-5)
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (425860/2016-7, 307027/2017-1, 2017-1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- multi-agent reinforcement learning, transfer learning, knowledge reuse, multi-agent systems, sample complexity
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