Learning and control in a chaotic system

Most real world problems can be solved at least partially by simple, nonadaptive means. When we want to use an adaptive learning technique like reinforcement learning for practical purposes, we need to be able to cooperate with one or several of these partial solutions. In this paper we consider the problem of making a reinforcement learning agent cooperate with a hand-crafted local controller and a global chaotic controller, and designing a regime that improves upon these controller.