One potential limitation of the ARMDP representation is the scalability and computational complexity when dealing with large state and action spaces. As the size of the state and action spaces increases, the number of possible transitions and reward dependencies grows exponentially, leading to challenges in solving the ILP efficiently.
To address this limitation, future research could focus on developing more efficient optimization algorithms tailored for large-scale ARMDP problems. Techniques like parallel computing, distributed optimization, or approximation methods could be explored to handle the computational demands of scaling up the ARMDP representation.
Another drawback could be the interpretability of the learned RMs in complex environments. As the number of hidden triggers and reward dependencies increases, understanding the underlying logic of the automaton may become challenging. Future research could investigate visualization techniques or explainable AI methods to enhance the interpretability of the learned RMs.