Propagation Networks for Model-Based Control Under Partial Observation
This addresses a practical limitation in robotics and AI systems where partial observability restricts the use of existing models like interaction networks.
The paper tackles the problem of learning dynamics simulators for model-based control under partial observation, introducing Propagation Networks (PropNet) that outperform existing learnable physics engines in forward simulation and achieve superior performance on control tasks compared to model-free deep reinforcement learning algorithms.
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. Experiments show that our propagation networks not only outperform current learnable physics engines in forward simulation, but also achieve superior performance on various control tasks. Compared with existing model-free deep reinforcement learning algorithms, model-based control with propagation networks is more accurate, efficient, and generalizable to new, partially observable scenes and tasks.