複合ポリシーと構成可能ポリシーを同時に学習する階層型強化学習
Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies
複数のサブタスク用ガウシアンポリシーを活性化ベクトルで合成し、複合ポリシーから得たオフポリシーデータで複合・構成可能ポリシーを同一学習過程で同時に獲得する手法を提案した。
著者: Domingo Esteban, Leonel Rozo, Darwin G. Caldwell
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
A common strategy to deal with the expensive reinforcement learning (RL) of complex tasks is to decompose them into a collection of subtasks that are usually simpler to learn as well as reusable for new problems. However, when a robot learns the policies for these subtasks, common approaches treat every policy learning process separately. Therefore, all these individual (composable) policies need to be learned before tackling the learning process of the complex task through policies composition. Moreover, such composition of individual policies is usually performed sequentially, which is not suitable for tasks that require to perform the subtasks concurrently. In this paper, we propose to combine a set of composable Gaussian policies corresponding to these subtasks using a set of activation vectors, resulting in a complex Gaussian policy that is a function of the means and covariances matrices of the composable policies. Moreover, we propose an algorithm for learning both compound and composable policies within the same learning process by exploiting the off-policy data generated from the compound policy. The algorithm is built on a maximum entropy RL approach to favor exploration during the learning process. The results of the experiments show that the experience collected with the compound policy permits not only to solve the complex task but also to obtain useful composable policies that successfully perform in their corresponding subtasks.
