Improving Reinforcement Learning results with Qualitative Spatial Representation

Author(s)

Homem, Thiago P. D. ; Perico, Danilo H. ; Santos, Paulo E. ; Costa, Anna H. R. ; Bianchi, Reinaldo A. C. ; IEEE

• Document type: Journal article
• Source: 2017 6TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS); v. N/A, p. 6-pg., 2017-01-01.

Abstract

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)

• FAPESP's process: 16/18792-9 - Describing, representing and solving spatial puzzles
• Grantee: Paulo Eduardo Santos
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
• FAPESP's process: 16/21047-3 - ALIS: Autonomous Learning in Intelligent System
• Grantee: Anna Helena Reali Costa
• Support Opportunities: Regular Research Grants