| Author(s): |
Da Silva, Felipe Leno
;
Reali Costa, Anna Helena
;
ACM
Total Authors: 3
|
| Document type: | Journal article |
| Source: | PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18); v. N/A, p. 9-pg., 2018-01-01. |
| Abstract | |
Autonomously learning a complex task takes a very long time for Reinforcement Learning (RL) agents. One way to learn faster is by dividing a complex task into several simple subtasks and organizing them into a Curriculum that guides Transfer Learning (TL) methods to reuse knowledge in a convenient sequence. However, previous works do not take into account the TL method to build specialized Curricula, leaving the burden of a careful subtask selection to a human. We here contribute novel procedures for: (i) dividing the target task into simpler ones under minimal human supervision; (ii) automatically generating Curricula based on object-oriented task descriptions; and (iii) using generated Curricula for reusing knowledge across tasks. Our experiments show that our proposal achieves a better performance using both manually given and generated subtasks when compared to the state-of-the-art technique in two different domains. (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 |