部分観測下のモデルベース制御のための伝播ネットワーク
Propagation Networks for Model-Based Control Under Partial Observation
部分観測環境でも使える学習可能な力学モデルPropNetを提案し、ペアワイズを超えた信号伝播を可能にして、順方向シミュレーションと制御タスクで既存手法を上回る性能を示した論文。
著者: Yunzhu Li, Jiajun Wu, Jun-Yan Zhu, Joshua B. Tenenbaum, Antonio Torralba, Russ Tedrake
分類: cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
