Linking Learning Fundamental Reinforcement Learning Concepts with Being Physically Active
Linking Learning Fundamental Reinforcement Learning Concepts with Being Physically Active
复制标题
将学习基本强化学习概念与身体活动联系起来
DOI：
10.1145/3545947.3576316
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Payton, Jamie
中科院分区：
文献类型：
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作者：
Annaluru, Ramakrishna Sai;Julien, Christine;Payton, Jamie
在本文中，我们定义了一个学习活动的小学体育课堂，同时从事学生的体育活动，同时向学生介绍强化学习的基本原则。强化学习是机器学习的一个子领域，其中一个独立的代理（在我们的活动中，一个学生）采取一些行动或一系列行动，并为所选择的行动获得奖励。虽然强化学习直观地映射到我们日常生活中的许多活动，但我们的学习活动涉及间谍游戏。学生创建间谍动作序列，根据其组成动作和执行顺序生成奖励。然后，学生们反复扩大他们的间谍行动，试图获得最大的奖励。游戏的构建将证明奖励虽然是确定性的，但并不总是遵循贪婪的模式，向学生介绍基本的算法原理。这种将身体活动与强化学习相结合的方法将人工智能教育与更广泛的计算和学生的日常生活联系起来。
In this paper, we define a learning activity for an elementary physical education classroom that simultaneously engages students in physical activity while introducing students to basic principles of reinforcement learning. Reinforcement learning is a sub-domain of machine learning in which an independent agent (in our activity, a student) takes some action or series of actions and receives a reward for the chosen action(s). While reinforcement learning intuitively maps to many activities in our daily lives, our learning activity involves a spy game. Students create sequences of spy moves that generate rewards based on their component moves and the orders in which they are performed. Students then iteratively expand their spy moves in an attempt to receive the maximum reward. The construction of the game will demonstrate that the rewards, while deterministic, do not always follow a greedy pattern, introducing students to basic algorithmic principles. Such an approach that combines physical activity with reinforcement learning connects artificial intelligence education within the broader scope of computing and students' everyday lives.