| Texto completo | |
| Autor(es): |
da Silva, Valdinei Freire
;
Koga, Marcelo Li
;
Cozman, Fabio Gagliardi
;
Reali Costa, Anna Helena
;
Behnke, S
;
Veloso, M
;
Visser, A
;
Xiong, R
Número total de Autores: 8
|
| Tipo de documento: | Artigo Científico |
| Fonte: | ROBOCUP 2013: ROBOT WORLD CUP XVII; v. 8371, p. 12-pg., 2014-01-01. |
| Resumo | |
In this paper we improve learning performance of a risk-aware robot facing navigation tasks by employing transfer learning; that is, we use information from a previously solved task to accelerate learning in a new task. To do so, we transfer risk-aware memoryless stochastic abstract policies into a new task. We show how to incorporate risk-awareness into robotic navigation tasks, in particular when tasks are modeled as stochastic shortest path problems. We then show how to use a modified policy iteration algorithm, called AbsProb-PI, to obtain risk-neutral and risk-prone memoryless stochastic abstract policies. Finally, we propose a method that combines abstract policies, and show how to use the combined policy in a new navigation task. Experiments validate our proposals and show that one can find effective abstract policies that can improve robot behavior in navigation problems. (AU) | |
| Processo FAPESP: | 11/19280-8 - CogBot: integrando informação perceptual e conhecimento semântico na robótica cognitiva |
| Beneficiário: | Anna Helena Reali Costa |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 12/02190-9 - Transferência de Conhecimento entre Tarefas no Aprendizado por Reforço |
| Beneficiário: | Marcelo Li Koga |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |