  Parameterized synthesis offers a solution to the problem of constructing
correct and verified controllers for parameterized systems. Such systems occur
naturally in practice (e.g., in the form of distributed protocols where the
amount of processes is often unknown at design time and the protocol must work
regardless of the number of processes). In this paper, we present a novel
learning based approach to the synthesis of reactive controllers for
parameterized systems from safety specifications. We use the framework of
regular model checking to model the synthesis problem as an infinite-duration
two-player game and show how one can utilize Angluin's well-known L* algorithm
to learn correct-by-design controllers. This approach results in a synthesis
procedure that is conceptually simpler than existing synthesis methods with a
completeness guarantee, whenever a winning strategy can be expressed by a
regular set. We have implemented our algorithm in a tool called L*-PSynth and
have demonstrated its performance on a range of benchmarks, including robotic
motion planning and distributed protocols. Despite the simplicity of L*-PSynth
it competes well against (and in many cases even outperforms) the
state-of-the-art tools for synthesizing parameterized systems.
