Reinforcement learning in large state spaces: Simulated robotic soccer as a testbed
Reinforcement learning in large state spaces: Simulated robotic soccer as a testbed
复制标题
大型状态空间中的强化学习：模拟机器人足球作为测试平台
DOI：
--
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
B. Manderick
中科院分区：
文献类型：
--
作者：
K. Tuyls;S. Maes;B. Manderick
大的状态空间和不完全的信息是两个突出的问题，在学习中的多智能体系统。在本文中，我们通过使用决策树和贝叶斯网络（BN）的组合来对环境和Q函数进行建模来解决这两个问题。模拟机器人足球作为一个测试床，因为有代理面临着大的状态空间和不完整的信息。这项研究的长期目标是定义通用技术，使代理在大规模的多代理系统中学习。
Large state spaces and incomplete information are two problems that stand out in learning in multi-agent systems. In this paper we tackle them both by using a combination of decision trees and Bayesian networks (BNs) to model the environment and the Q-function. Simulated robotic soccer is used as a testbed, since there agents are faced with both large state spaces and incomplete information. The long-term goal of this research is to define generic techniques that allow agents to learn in large-scaled multi-agent systems.