連続メモリ状態を持つ深層ニューラルネットワーク方策の学習
Learning Deep Neural Network Policies with Continuous Memory States
方策が読み書きできる連続値メモリ状態を状態・行動空間に追加し、軌道最適化と教師あり学習を組み合わせたguided policy searchで、部分観測下のロボット操作・ナビゲーションタスクを記憶が必要な方策として学習する手法を提案した。
著者: Marvin Zhang, Zoe McCarthy, Chelsea Finn, Sergey Levine, Pieter Abbeel
分類: cs.LG, cs.RO
原文アブストラクト
Policy learning for partially observed control tasks requires policies that can remember salient information from past observations. In this paper, we present a method for learning policies with internal memory for high-dimensional, continuous systems, such as robotic manipulators. Our approach consists of augmenting the state and action space of the system with continuous-valued memory states that the policy can read from and write to. Learning general-purpose policies with this type of memory representation directly is difficult, because the policy must automatically figure out the most salient information to memorize at each time step. We show that, by decomposing this policy search problem into a trajectory optimization phase and a supervised learning phase through a method called guided policy search, we can acquire policies with effective memorization and recall strategies. Intuitively, the trajectory optimization phase chooses the values of the memory states that will make it easier for the policy to produce the right action in future states, while the supervised learning phase encourages the policy to use memorization actions to produce those memory states. We evaluate our method on tasks involving continuous control in manipulation and navigation settings, and show that our method can learn complex policies that successfully complete a range of tasks that require memory.
