Thank you for this. It appears to be an exceedingly elegant take on modeling uncertainty in planning and search. There's something quite potent about changing the task to be variable-length, but also forcing the agent to account for its current situation instead of taking it for granted. This allows the agent to react and generalize way better along its path, even in the face of unforeseen challenges.

I assume this is set up so that all tasks are treated as variable horizon, and the current state as a consequence of preceding actions. I agree it would be nice to see the code.