Biography

KENJI DOYA took BS in 1984, MS in 1986, and Ph.D. in 1991 at U. Tokyo. He became a research associate at U. Tokyo in 1986, U. C. San Diego in 1991, and Salk Institute in 1993. He joined Advanced Telecommunications Research International (ATR) in 1994 and became the head of Computational Neurobiology Department, ATR Computational Neuroscience Laboratories in 2003. In 2004, he was appointed as the Principal Investigator of Neural Computation Unit, Okinawa Institute of Science and Technology (OIST) and started Okinawa Computational Neuroscience Course (OCNC) as the chief organizer. As OIST established itself as a graduate university in 2011, he became a Professor and served as the Vice Provost for Research. He serves as the Co-Editor in Chief of Neural Networks since 2008 and a board member of Japanese Neural Network Society (JNNS) and Japan Neuroscience Society (JNSS). He served as the Program Co-Chair of International Conference on Neural Information Processing (ICONIP) in 2007 and 2016, the Program Chair of JNSS meeting in 2010, and the General Chair of JNNS meeting in 2011 and 2018. He received Tsukahara Award and JSPS Award in 2007, MEXT Prize for Science and Technology in 2012, and Donald O. Hebb Award in 2018. He lead the MEXT project “Prediction and Decision Making” from 2011 to 2016 and currently leads a new MEXT project “Artificial Intelligence and Brain Science”. He is interested in understanding the functions of basal ganglia and the cortical circuit based on the theory of reinforcement learning and Bayesian inference. Contact: doya@oist.jp, Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna, Okinawa 904-0495, Japan.

Abstract

Evolution of extrinsic and intrinsic reward functions

What is the origin of rewards for humans and animals? While some rewards like food and affection are directly linked to survival and reproduction, others are indirectly linked by promoting exploration and learning. We showed in a colony of mobile robots that supplementary reward functions promoting energy capturing and gene transfer can be shaped by embodied evolution (Elfwing et al., 2011). Here we consider how this can be extended to intrinsic rewards for model-free and model-based reinforcement learning agents.