Reinforcement learning models are not typically implemented with varying number of actions depending on the state. However, our models works well with varying actions. Therefore, this setup might be helpful in building models for similar use cases. We later present our results with synthesizability as determined by the SYBA 40 classifier. In principle, any scoring function would work here, but it requires significant training time and tuning of the model's hyperparameters.