| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Sao Paulo, Dept Comp Engn, Sao Paulo, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH; v. 64, p. 645-703, 2019. |
| Web of Science Citations: | 2 |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 18/00344-5 - Reusing previous task solutions in multiagent reinforcement learning |
| Grantee: | Felipe Leno da Silva |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| FAPESP's process: | 16/21047-3 - ALIS: Autonomous Learning in Intelligent System |
| Grantee: | Anna Helena Reali Costa |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 15/16310-4 - Transfer Learning in Reinforcement Learning Multi-Agent Systems |
| Grantee: | Felipe Leno da Silva |
| Support Opportunities: | Scholarships in Brazil - Doctorate |