以部分观察和机械约束为多人建模的分散的政策学习
Keisuke Fujii1, Naoya Takeishi2, Yoshinobu Kawahara3
1Graduate School of Informatics, Nagoya University, Nagoya, Aichi, Japan; Center for Advanced Intelligence Project, RIKEN, Osaka, Japan; PRESTO, Japan Science and Technology Agency, Tokyo, Japan.
概括
这项研究引入了一种新的方法,通过结合部分观察和机械约束来理解多代理行为. 该方法提高了模拟中的生物可信性和预测准确性.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 计算生物学 计算生物学
背景情况:
- 提取现实世界的多代理行为规则是具有挑战性的.
- 传统的数据驱动模型往往缺乏生物可信性和解释性,因为它们忽视了代理的局限性.
研究的目的:
- 开发生物可信的顺序生成模型,用于多代理行为.
- 以分散的方式将部分观测和机械约束纳入.
主要方法:
- 制定为一个分散的多代理模仿学习问题.
- 利用二元部分观测和分散的政策模型.
- 雇佣了具有物理和生物机械惩罚的等级变异性循环神经网络.
主要成果:
- 在现实世界篮球和足球数据集上表现出有效性.
- 在限制违规减少,长期轨迹预测和处理部分观测方面展示了改进.
- 验证了预测行为的生物可信性和解释性.
结论:
- 拟议的方法有效地模拟了代理人的认知和身体动态.
- 该方法作为一个强大的多代理模拟器,可以从现实数据中生成现实的轨迹.
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