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Improving Reinforcement Learning results with Qualitative Spatial Representation

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Autor(es):
Homem, Thiago P. D. ; Perico, Danilo H. ; Santos, Paulo E. ; Costa, Anna H. R. ; Bianchi, Reinaldo A. C. ; IEEE
Número total de Autores: 6
Tipo de documento: Artigo Científico
Fonte: 2017 6TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS); v. N/A, p. 6-pg., 2017-01-01.
Resumo

Reinforcement learning and Qualitative Spatial Reasoning methods have been successfully applied to create agents able to solve Artificial Intelligence problems in games, robotics, simulated or real. Generally, reinforcement learning methods represent the objects' position as quantitative values, performing the experiments considering these values. However, the human commonsense understanding of the world is qualitative. This work proposes a hybrid method, that uses a qualitative formalism with reinforcement learning, named QRL, and is able to get better results than traditional methods. We have applied this proposal in the robot soccer domain and compared the results with traditional reinforcement learning method. The results show that, by using a qualitative spatial representation with reinforcement learning, the agent can learn optimal policies and perform more goals than quantitative representation. (AU)

Processo FAPESP: 16/18792-9 - Descrição, representação e solução de jogos espaciais
Beneficiário:Paulo Eduardo Santos
Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
Processo FAPESP: 16/21047-3 - ALIS: Aprendizado Autônomo em Sistemas Inteligentes
Beneficiário:Anna Helena Reali Costa
Modalidade de apoio: Auxílio à Pesquisa - Regular