Self-referential event lists for self-organizing modular reinforcement learning

Abstract

Hybrid systems consisting of model-based and model-free systems will be engaged in the behavior/dialog control systems of future robots/agents to satisfy several user's requirements and simultaneously cope with diverse and unexpected situations. We have constructed a modular neural network model based on reinforcement learning for model-free learning. For an effective hybrid system, the model-free learning system should be aware of the current targets. This can be achieved by automatically acquiring a list of important sequential events. We propose a basic mechanism that can automatically acquire the list of sequential events with confidence measures reflecting current situations. © 2009 Springer Berlin Heidelberg.