MAXQ, a hierarchical reinforcement learning method for multi-agent system, is proposed in recent years.
MAXQ分层多智能体学习方法是近年来被提出的一种新方法。
Hierarchical reinforcement learning is one of the important methods for solving large scale learning space problems.
分层式强化学习是解决强化学习问题中大规模学习空间问题的一种重要方法。
Hierarchical reinforcement learning (HRL) was presented to combat the curse of dimensionality, and has made great progresses.
分层强化学习(HRL)是为解决强化学习的维数灾问题而提出的，并取得了显著进展。
According to the problem of mobile robot navigation in the unknown environment, a hybrid control method based on hierarchical reinforcement learning (HRL) is proposed.
针对未知环境下的移动机器人导航问题，本文提出了一种基于分层式强化学习的混合式控制方法。
A fighter safe landing lateral-directional control method is presented based on reinforcement learning algorithm (RL), using hierarchical control of dynamical large-scale systems theory.
基于大系统递阶控制思想，提出了一种运用再励学习算法设计歼击机自动着陆横侧向协调控制系统的方法。
A fighter safe landing lateral-directional control method is presented based on reinforcement learning algorithm (RL), using hierarchical control of dynamical large-scale systems theory.
基于大系统递阶控制思想，提出了一种运用再励学习算法设计歼击机自动着陆横侧向协调控制系统的方法。
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