Reducing Training Environments in Evolutionary Robotics Through Ecological Modularity

Abstract

Due to the large number of evaluations required, evolutionary robotics experiments are generally conducted in simulated environments. One way to increase the generality of a robot’s behavior is to evolve it in multiple environments. These environment spaces can be defined by the number of free parameters (f) and the number of variations each free parameter can take (n). Each environment space then has $n^f$ individual environments. For a robot to be fit in the environment space it must perform well in each of the $n^f$ environments. Thus the number of environments grows exponentially as n and f are increased. To mitigate the problem of having to evolve a robot in each environment in the space we introduce the concept of ecological modularity. Ecological modularity is here defined as the robot’s modularity with respect to free parameters in its environment space. We show that if a robot is modular along m of the free parameters in its environment space, it only needs to be evolved in $n^{f-m+1}$ environments to be fit in all of the $n^f$ environments. This work thus presents a heretofore unknown relationship between the modularity of an agent and its ability to generalize evolved behaviors in new environments.