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A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems
Open accessAbstract
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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Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., & Mordatch, I. (2017) · arXiv · doi:10.48550/arxiv.1706.02275 · Cited by 3,377
Evolving Neural Networks through Augmenting Topologies
Stanley, K. O., & Miikkulainen, R. P. (2002) · Evolutionary Computation · doi:10.1162/106365602320169811 · Cited by 3,269
A survey of robot learning from demonstration
Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.10.024 · Cited by 3,227
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
Research works printed but not linked: 17
- Abel, D., Salvatier, J., Stuhlmüller, A., & Evans, O. (2016). Agent-Agnostic Human-in- the-Loop Reinforcement Learning. In Proceedings of the NIPS Future of Interactive Learning Machines Workshop.
- Barto, A. G., Thomas, P. S., & Sutton, R. S. (2017). Some Recent Applications of Re- inforcement Learning. In Proceedings of the 18th Yale Workshop on Adaptive and Learning Systems.
- Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016). OpenAI Gym. CoRR, abs/1606.01540. · arXiv
- Castaneda, A. O. (2016). Deep Reinforcement Learning Variants of Multi-Agent Learning Algorithms. Ph.D. thesis, University of Edinburgh.
- Diuk, C. (2009). An Object-Oriented Representation for Efficient Reinforcement Learning. Ph.D. thesis, Rutgers University.
- Du, Y., de la Cruz, G. V., Irwin, J., & Taylor, M. E. (2016). Initial Progress in Transfer for Deep Reinforcement Learning Algorithms. In Proceedings of Deep Reinforcement Learning: Frontiers and Challenges Workshop at IJCAI.
- Fitzgerald, T., Bullard, K., Thomaz, A., & Goel, A. (2016). Situated Mapping for Transfer Learning. In Proceedings of the 4th Annual Conference on Advances in Cognitive Systems, pp. 1-14.
- PMLR.
- Isele, D., Rostami, M., & Eaton, E. (2016). Using Task Features for Zero-Shot Knowl- edge Transfer in Lifelong Learning. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1620-1626.
- Judah, K., Fern, A. P., Dietterich, T. G., & Tadepalii, P. (2014). Active Imitation Learning: Formal and Practical Reductions to I.I.D. Learning. Journal of Machine Learning Research (JMLR), 15 (1), 3925-3963.
- Kelly, S., & Heywood, M. I. (2015). Knowledge Transfer from Keepaway Soccer to Half- Field Offense through Program Symbiosis: Building Simple Programs for a Complex Task. In Proceedings of the 17th Conference on Genetic and Evolutionary Computation (GECCO), pp. 1143-1150. ACM.
- Peng, B., MacGlashan, J., Loftin, R., Littman, M. L., Roberts, D. L., & Taylor, M. E. (2016b). An Empirical Study of Non-expert Curriculum Design for Machine Learners. In Proceedings of the IJCAI Interactive Machine Learning Workshop.
- Perico, D. H., & Bianchi, R. A. C. (2013). Use of Heuristics from Demonstrations to Speed Up Reinforcement Learning. In Proceedings of the 12th Brazilian Symposium on Intelligent Automation (SBAI).
- Ramakrishnan, R., Narasimhan, K., & Shah, J. (2016). Interpretable Transfer for Reinforce- ment Learning based on Object Similarities. In Proceedings of the IJCAI Interactive Machine Learning Workshop.
- Schaal, S. (1997). Learning from Demonstration. In Advances in Neural Information Pro- cessing Systems (NIPS), pp. 1040-1046.
- Silva, F. L. D., & Costa, A. H. R. (2017b). Towards Zero-Shot Autonomous Inter-Task Map- ping through Object-Oriented Task Description. In Proceedings of the 1st Workshop on Transfer in Reinforcement Learning (TiRL).
- Watkins, C. J., & Dayan, P. (1992). Q-Learning. Machine Learning, 8 (3), 279-292.
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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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