Planning using online evolutionary overfitting

Abstract

Biological systems tend to perform a range of tasks of extreme variability with extraordinary efficiency. It has been argued that a plausible scenario for achieving such versatility is explicitly learning a forward model. We perform a set of experiments using the original and a modified version of a classic reinforcement learning task, the mountain car problem, using a number of agents that encode both a direct and an abstracted version of a forward model. The results suggest that superior performance can be achieved if the forward model can be exploited in real-time by an agent that has already internalised a model-free control function.