An extended Q learning system with emotion state to make up an agent with individuality
An extended Q learning system with emotion state to make up an agent with individuality
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DOI：
10.5220/0005616500700078
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
M. Obayashi;Shunsuke Uto;T. Kuremoto;S. Mabu;Kunikazu Kobayashi
中科院分区：
文献类型：
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作者：
M. Obayashi;Shunsuke Uto;T. Kuremoto;S. Mabu;Kunikazu Kobayashi
近年来，结合神经科学知识的智能机器人的研究已经被积极地进行。特别是，已经看到了很多研究人员利用强化学习，特别是，“带有情感的强化学习方法”，到目前为止已经提出，是非常有吸引力的方法，因为它使我们能够实现复杂的对象，这是传统的强化学习方法无法实现的，考虑到情感。在本文中，我们提出了一个扩展的强化（Q）学习系统与杏仁核（情绪）模型，以弥补每个代理的个人情绪。此外，通过计算机模拟，所提出的方法被应用到目标搜索问题，包括各种不同的解决方案，它发现，每个代理是能够有每个单独的解决方案。
Recently, researches for the intelligent robots incorporating knowledge of neuroscience have been actively carried out. In particular, a lot of researchers making use of reinforcement learning have been seen, especially, “Reinforcement learning methods with emotions”, that has already proposed so far, is very attractive method because it made us possible to achieve the complicated object, which could not be achieved by the conventional reinforcement learning method, taking into account of emotions. In this paper, we propose an extended reinforcement (Q) learning system with amygdala (emotion) models to make up individual emotions for each agent. In addition, through computer simulations that the proposed method is applied to the goal search problem including a variety of distinctive solutions, it finds that each agent is able to have each individual solution.