| Author(s): |
Da Silva, Felipe Leno
;
Taylor, Matthew E.
;
Reali Costa, Anna Helena
;
Lang, J
Total Authors: 4
|
| Document type: | Journal article |
| Source: | PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE; v. N/A, p. 7-pg., 2018-01-01. |
| Abstract | |
Autonomous agents are increasingly required to solve complex tasks; hard-coding behaviors has become infeasible. Hence, agents must learn how to solve tasks via interactions with the environment. In many cases, knowledge reuse will be a core technology to keep training times reasonable, and for that, agents must be able to autonomously and consistently reuse knowledge from multiple sources, including both their own previous internal knowledge and from other agents. In this paper, we provide a literature review of methods for knowledge reuse in Multiagent Reinforcement Learning. We define an important challenge problem for the AI community, survey the existent methods, and discuss how they can all contribute to this challenging problem. Moreover, we highlight gaps in the current literature, motivating "low-hanging fruit" for those interested in the area. Our ambition is that this paper will encourage the community to work on this difficult and relevant research challenge. (AU) | |
| 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 |
| 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 |