Transfer Learning Method Using Ontology for Heterogeneous Multi-agent Reinforcement Learning
Transfer Learning Method Using Ontology for Heterogeneous Multi-agent Reinforcement Learning
复制标题
使用本体进行异构多智能体强化学习的迁移学习方法
DOI：
10.14569/ijacsa.2014.051022
复制
发表时间：
2014
影响因子：
0.9
通讯作者：
Tsuyoshi Suzuki
中科院分区：
文献类型：
--
作者：
H. Kono;A. Kamimura;K. Tomita;Y. Murata;Tsuyoshi Suzuki
本文提出了一个名为知识共创框架（KCF）的框架，用于使用迁移学习方法的异构多智能体机器人系统。最近在现实情况中研究了利用强化学习和迁移学习方法的多智能体机器人系统（MARS）。在 MARS 中，自主代理通过多代理强化学习自主获取行为，迁移学习方法可以重用其他机器人行为的知识，例如协作行为。然而，这些方法尚未得到充分和系统的讨论。为了解决这个问题，KCF 利用了迁移学习方法和云计算资源。在之前的研究中，我们开发了基于本体的任务间映射作为分层迁移学习（HTL）方法的核心技术，并研究了其在动态多智能体环境中的有效性。 HTL方法通过本体论方法对获得的知识进行层次化抽象。在这里，我们使用考虑两种类型本体的基本实验设置来评估 HTL 的有效性：动作和状态。
This paper presents a framework, called the knowledge co-creation framework (KCF), for heterogeneous multiagent robot systems that use a transfer learning method. A multiagent robot system (MARS) that utilizes reinforcement learning and a transfer learning method has recently been studied in realworld situations. In MARS, autonomous agents obtain behavior autonomously through multi-agent reinforcement learning and the transfer learning method enables the reuse of the knowledge of other robots’ behavior, such as for cooperative behavior. Those methods, however, have not been fully and systematically discussed. To address this, KCF leverages the transfer learning method and cloud-computing resources. In prior research, we developed ontology-based inter-task mapping as a core technology for hierarchical transfer learning (HTL) method and investigated its effectiveness in a dynamic multi-agent environment. The HTL method hierarchically abstracts obtained knowledge by ontological methods. Here, we evaluate the effectiveness of HTL with a basic experimental setup that considers two types of ontology: action and state.