Automatic Generation and Learning of Finite-State Controllers

Abstract

We propose a method for generating and learning agent controllers, which combines techniques from automated planning and reinforcement learning. An incomplete description of the domain is first used to generate a non-deterministic automaton able to act (sub-optimally) in the given environment. Such a controller is then refined through experience, by learning choices at non-deterministic points. On the one hand, the incompleteness of the model, which would make a pure-planning approach ineffective, is overcome through learning. On the other hand, the portion of the domain available drives the learning process, that otherwise would be excessively expensive. Our method allows to adapt the behavior of a given planner to the environment, facing the unavoidable discrepancies between the model and the environment. We provide quantitative experiments with a simulator of a mobile robot to assess the performance of the proposed method.