【摘要】 在学习过程中自动发现有用的Subgoal并创建Option,对提高强化学习的学习性能有着重要意义.提出了一种基于k-聚类的Subgoal自动发现算法,该算法能通过对在线获取的少量路径数据进行聚类的方法抽取出Subgoal.实验表明,该算法能有效地发现所有符合要求的Subgoal,与Q-学习和基于多样性密度的强化学习算法相比,用该算法发现Subgoal并创建Option的强化学习算法能有效提高A-gent的学习速度.更多还原

【Abstract】 Discovering useful subgoals and creating options while learning is important to improve the learning performance of agent in hierarchical reinforcement learning. A new subgoal discovery algorithm based on k-cluster is proposed which can extract subgoals from the set of trajectories collected online by clustering them. The results of experiment show that the algorithm can find all sobgoals quite efficiently, and the hierarchical reinforcement learning with k-cluster algorithm has better learning performance than Q-learning and hierarchical reinforcement learning with diverse density algorithm.更多还原