Evolving Multimodal Behavior Through Subtask and Switch Neural Networks (2014)
While neuroevolution has been used successfully to discover effective control policies for intelligent agents, it has been difficult to evolve behavior that is multimodal, i.e. consists of distinctly different behaviors in different situations. This article proposes a new method, Modular NeuroEvolution of Augmenting Topologies (ModNEAT), to meet this challenge. ModNEAT decomposes complex tasks into tractable subtasks and utilizes neuroevolution to learn each subtask. Switch networks are evolved with the subtask networks to arbitrate among them and thus combine separate subtask networks into a complete hierarchical policy. Further, the need for new subtask modules is detected automatically by monitoring fitness of the agent population. Experimental results in the machine learning game of OpenNERO showed that ModNEAT outperforms the non-modular rtNEAT in both agent fitness and training efficiency.
In Proceedings of The Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE 14), 2014.

Xun Li Ph.D. Alumni xun bhsfer [at] cs utexas edu
Risto Miikkulainen Faculty risto [at] cs utexas edu