\section{Conclusion}
In this paper we revisited the optimal control of Markovian Jump Linear Systems as a benchmark for further understanding of policy-based RL algorithms and hybrid MDPs.
We discussed how to set up the policy parameterization for such hybrid MDPs, and present an efficient data-driven implementation of the natural policy gradient method for learning optimal state-feedback controllers of unknown MJLSs. 

We demonstrated the performance of the model-free natural policy gradient method on different example systems.
Our results suggest that it is promosing to apply policy-based RL methods for optimal control of large scale switching systems, where the computational complexity grows as the system size increases.
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