Computational Models of the Amygdala and the Orbitofrontal Cortex: A Hierarchical Reinforcement Learning System for Robotic Control

Authors:
Weidong Zhou;Richard Coggins
Affiliations:
-;-
Venue:
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2002

Citing 4
Cited 2

Technical Note: \cal Q-Learning

Machine Learning
Computer graphics (2nd ed. in C): principles and practice

Computer graphics (2nd ed. in C): principles and practice
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Artificial Intelligence
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning intentional behavior in the K-model of the amygdala and entorhinal cortex with the cortico-hyppocampal formation

Neurocomputing

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Abstract

This paper presents biologically plausible computational models of brain areas involved in emotion processing and the decision-making process. In the models, the amygdala, the orbotofrontal cortex (OFC) and the basal ganglia work together as a multiple-level hierarchical reinforcement learning system. The amygdala decodes sensory cues into reward-related variables providing a reward-related abstract representation for the decision making process in the OFC, while the basal ganglia learn and execute subtask policies. Here we hypothesize how the amygdala may learn these representations. The models have been implemented in software to control a Khepera robot in a physical environment designed for comparison with animal behaviours. We show that the representation of principal emotion components in the reward function may lead to a more efficient learning algorithm than general Q learning.