Authors:
Masanao Obayashi
1
;
Shunsuke Uto
1
;
Takashi Kuremoto
1
;
Shingo Mabu
1
and
Kunikazu Kobayashi
2
Affiliations:
1
Graduate School of Science and Engineering and Yamaguchi University, Japan
;
2
School of Information Science and Technology and Aichi Prefectual University, Japan
Keyword(s):
Reinforcement Learning, Amygdala, Emotional Model, Q Learning, Individuality.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Higher Level Artificial Neural Network Based Intelligent Systems
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Recently, researches for the intelligent robots incorporating knowledge of neuroscience have been actively
carried out. In particular, a lot of researchers making use of reinforcement learning have been seen,
especially, "Reinforcement learning methods with emotions", that has already proposed so far, is very
attractive method because it made us possible to achieve the complicated object, which could not be
achieved by the conventional reinforcement learning method, taking into account of emotions. In this paper,
we propose an extended reinforcement (Q) learning system with amygdala (emotion) models to make up
individual emotions for each agent. In addition, through computer simulations that the proposed method is
applied to the goal search problem including a variety of distinctive solutions, it finds that each agent is able
to have each individual solution.