誘導コスト学習：方策最適化による深層逆最適制御
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
示範から非線形なコスト関数をニューラルネットで学習する逆最適制御手法を提案し、高次元ロボットのトルク制御で従来法より高効率・高複雑タスクを実現した。
著者: Chelsea Finn, Sergey Levine, Pieter Abbeel
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Reinforcement learning can acquire complex behaviors from high-level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems. Our method addresses two key challenges in inverse optimal control: first, the need for informative features and effective regularization to impose structure on the cost, and second, the difficulty of learning the cost function under unknown dynamics for high-dimensional continuous systems. To address the former challenge, we present an algorithm capable of learning arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering. To address the latter challenge, we formulate an efficient sample-based approximation for MaxEnt IOC. We evaluate our method on a series of simulated tasks and real-world robotic manipulation problems, demonstrating substantial improvement over prior methods both in terms of task complexity and sample efficiency.
