NeurIPS Camready: Influence-Augmented Online Planning

The camready version of Influence-Augmented Online Planning for Complex Environments is now available.

In this work, we show that by learning approximate representations of influence, we can speed up online planning (POMCP) sufficiently to get better performance when the time for online decision making is constrained.

AAMAS: Maximizing Information Gain via Prediction Rewards

This paper tackles the problem of active perception: taking actions to minimize one’s uncertainty. It further formalizes the link between information gain and prediction rewards, and uses this to propose a deep-learning approach to optimize active perception from a data set, thus obviating the need for a complex POMDP model.