Handle datasets with no terminating states

Imagine that we use ReAgent to train a personalization policy, and the workflow is as follows:
1. We collect a number of user interaction histories (episodes) and train a DQN model in offline (Batch RL) mode.
2. Some time later, we collect additional transitions for the same users and want to update the DQN model by feeding new transitions into ReAgent (incrementally, without retraining from scratch).

The question is how to do this correctly to handle the initial conditions - in DQN, it is assumed that Q(s,a)=0 for the final states of the episodes, but we extend the episodes with new transitions at each update. Does ReAgent handle this correctly?