| Autor(es): |
Da Silva, Felipe Leno
;
Assoc Comp Machinery
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS; v. N/A, p. 2-pg., 2019-01-01. |
| Resumo | |
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) | |
| Processo FAPESP: | 15/16310-4 - Transferência de Conhecimento no Aprendizado por Reforço em Sistemas Multiagentes |
| Beneficiário: | Felipe Leno da Silva |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |
| Processo FAPESP: | 16/21047-3 - ALIS: Aprendizado Autônomo em Sistemas Inteligentes |
| Beneficiário: | Anna Helena Reali Costa |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 18/00344-5 - Reusando soluções de tarefas prévias em aprendizado por reforço multiagente |
| Beneficiário: | Felipe Leno da Silva |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Doutorado |