| Author(s): |
Da Silva, Felipe Leno
;
Assoc Comp Machinery
Total Authors: 2
|
| Document type: | Journal article |
| Source: | AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS; v. N/A, p. 2-pg., 2019-01-01. |
| Abstract | |
Reinforcement learning methods have successfully been applied to build autonomous agents that solve challenging sequential decision-making problems. However, agents need a long time to learn a task, especially when multiple autonomous agents are in the environment. This research aims to propose a Transfer Learning framework to accelerate learning by combining two knowledge sources: (i) previously learned tasks; and (ii) advice from a more experienced agent. The definition of such framework requires answering several challenging research questions, including: How to abstract and represent knowledge, in order to allow generalization and posterior reuse?, How and when to transfer and receive knowledge in an efficient manner?, and How to consistently combine knowledge from several sources? (AU) | |
| 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: | 16/21047-3 - ALIS: Autonomous Learning in Intelligent System |
| Grantee: | Anna Helena Reali Costa |
| Support Opportunities: | Regular Research Grants |
| 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 |