基于任务完成评价的开发率动态调整研究

Masashi Sugimoto
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引用次数: 1

摘要

到目前为止,在强化学习中,被称为探索的随机行为的比例通常没有被动态调整。然而,这个比率将是强化学习中的一个性能指标。在本研究中,将建议智能体从另一个智能体的任务成就评价中学习信息。通过该方法,可以根据其他智能体的行为动态调整探索比例。在人类生活中,“氛围”将作为一种交流方式而存在。例如,根据经验,人们会受到“严肃气氛”的影响,例如在工作或参加考试的情况下。本研究将定义这种氛围作为agent完成任务的动机。此外,在本研究中,将重点研究当另一个代理将解决任务时,代理的行动决策。换句话说,如果其他智能体已经找到了最优解,那么一个智能体将试图找到一个最优解。本文提出了基于其他代理行为的行动决策。此外,本研究还以迷宫问题为例讨论了有效性问题。特别是“任务完成的数量”和“对任务完成的影响”,以及如何实现任务量化将被重点关注。结果表明,该方法能很好地受其他agent行为的影响。
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A Study for Dynamically Adjustmentation for Exploitation Rate using Evaluation of Task Achievement
Until now, in reinforcement learning, a ratio of a random action as known as exploration often has not been adjusted dynamically. However, this ratio will be an index of performance in the reinforcement learning. In this study, agents learn using information from the evaluation of achievement for task of another agent, will be suggested. From this proposed method, the exploration ratio will be adjusted from other agents’ behavior, dynamically. In Human Life, an “atmosphere” will be existed as a communication method. For example, empirically, people will be influenced by “serious atmosphere,” such as in the situation of working, or take an examination. In this study, this atmosphere as motivation for task achievement of agent will be defined. Moreover, in this study, agent’s action decision when another agent will be solved the task, will be focused on. In other words, an agent will be trying to find an optimal solution if other agents have been found an optimal solution. In this paper, we propose the action decision based on other agent’s behavior. Moreover, in this study, we discuss effectiveness using the maze problem as an example. In particular, “number of task achievement” and “influence for task achievement,” and how to achieve the task quantitative will be focused. As a result, we confirmed that the proposed method is well influenced from other agent’s behavior.
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