Improving reinforcement learning results with qualitative spatial representation
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2017-10-02
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HOMEM, T. P. D.
PERICO, D. H.
Paulo Santos
COSTA, A. H. R.
Reinaldo Bianchi
PERICO, D. H.
Paulo Santos
COSTA, A. H. R.
Reinaldo Bianchi
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Proceedings - 2017 Brazilian Conference on Intelligent Systems, BRACIS 2017
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HOMEM, T. P. D.; PERICO, D. H.; SANTOS, P.; COSTA, A. H. R.; BIANCHI, R. Improving reinforcement learning results with qualitative spatial representation. Proceedings - 2017 Brazilian Conference on Intelligent Systems, BRACIS 2017, v. 2018-January, p. 151-156, oct. 2017.
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© 2017 IEEE.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 humancommonsense 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.