  Giving autonomous agents the ability to forecast their own outcomes and
uncertainty will allow them to communicate their competencies and be used more
safely. We accomplish this by using a learned world model of the agent system
to forecast full agent trajectories over long time horizons. Real world systems
involve significant sources of both aleatoric and epistemic uncertainty that
compound and interact over time in the trajectory forecasts. We develop a deep
generative world model that quantifies aleatoric uncertainty while
incorporating the effects of epistemic uncertainty during the learning process.
We show on two reinforcement learning problems that our uncertainty model
produces calibrated outcome uncertainty estimates over the full trajectory
horizon.
