Title: Propagation Networks for Model-Based Control Under Partial Observation,
Abstract: There has been an increasing interest in learning dynamics simulators for
model-based control. Compared with off-the-shelf physics engines, a learnable
simulator can quickly adapt to unseen objects, scenes, and tasks. However,
existing models like interaction networks only work for fully observable
systems; they also only consider pairwise interactions within a single time
step, both restricting their use in practical systems. We introduce Propagation
Networks (PropNet), a differentiable, learnable dynamics model that handles
partially observable scenarios and enables instantaneous propagation of signals
beyond pairwise interactions. With these innovations, our propagation networks
not only outperform current learnable physics engines in forward simulation,
but also achieves superior performance on various control tasks. Compared with
existing deep reinforcement learning algorithms, model-based control with
propagation networks is more accurate, efficient, and generalizable to novel,
partially observable scenes and tasks.