Robust Population Coding in Free-Energy-Based Reinforcement Learning

Authors:
Makoto Otsuka;Junichiro Yoshimoto;Kenji Doya
Affiliations:
Initial Research Project, Okinawa Institute of Science and Technology, , Uruma, Japan 904-2234 and Graduate School of Information Science, Nara Institute of Science and Technology, , Ikoma, Japan ...;Initial Research Project, Okinawa Institute of Science and Technology, , Uruma, Japan 904-2234 and Graduate School of Information Science, Nara Institute of Science and Technology, , Ikoma, Japan ...;Initial Research Project, Okinawa Institute of Science and Technology, , Uruma, Japan 904-2234 and Graduate School of Information Science, Nara Institute of Science and Technology, , Ikoma, Japan ...
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

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Abstract

We investigate the properties of free-energy-based reinforcement learning using a new experimental platform called the digit floor task. The simulation results showed the robustness of the reinforcement learning method against noise applied in both the training and testing phases. In addition, reward-dependent and reward-invariant representations were found in the distributed activation patterns of hidden units. The representations coded in a distributed fashion persisted even when the number of hidden nodes were varied.