Combining Reinforcement Learning with a Local Control Algorithm

We explore combining reinforcement learning with a hand-crafted local controller in a manner suggested by the chaotic control algorithm of Vincent, Schmitt and Vincent (1994). A closedloop controller is designed using conventional means that creates a domain of attraction about a target state. Chaotic behavior is used or induced to bring the system into this region, at which time the local controller is turned on to bring the system to the target state and stabilize it there. We describe experiments in which we use reinforcement learning instead of, and in addition to, chaotic behavior to learn an efficient policy for driving the system into the local controller’s domain of attraction. Using a simulated double pendulum, we illustrate how this method allows reinforcement learning to be effective in a problem that cannot be easily solved by reinforcement learning alone, and we show how reinforcement learning can improve upon the chaotic control algorithm when the domain of attraction can only be approximately determined. Similar results are shown using the Henon map. This is a simple and effective way of extending reinforcement learning to more difficult problems.