Informed POMDP: Leveraging Additional Information in Model-Based RL

Understand

In this work, we generalize the problem of learning through interaction in a POMDP by accounting for eventual additional information available at training time.

• First, we introduce the informed POMDP, a new learning paradigm offering a clear distinction between the information at training and the observation at execution.
• Next, we propose an objective that leverages this information for learning a sufficient statistic of the history for the optimal control.
• We then adapt this informed objective to learn a world model able to sample latent trajectories.