Task decomposition with neuroevolution in extended predator-prey domain

Learning complex behaviour is a difficult task for any artificial agent. Decomposing a task into multiple sub-tasks, learning the sub-tasks separately, and then learning to use them as a whole is a natural way to reduce the dimensionality and complexity of the task function. This approach is demonstrated on a predator agent in the predator-prey-hunter domain. This extended domain has a new agent, a ‘hunter’, that chases the predators. The evading and chasing behaviours are learnt as separate sub-tasks by separate networks using the NEAT neuro-evolution method. A separate network is then evolved to use these networks based on the situation. Task decomposition using this approach performs significantly better in the predator-prey-hunter domain compared to a monolithic network evolved directly on the whole task.