  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. Finally, we empirically show a learning speed improvement in
several environments using this informed world model in the Dreamer algorithm.
These results and the simplicity of the proposed adaptation advocate for a
systematic consideration of eventual additional information when learning in a
POMDP using model-based RL.
