• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Control, Automation and Robotics >Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent Domains
【24h】

Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent Domains

机译：合作伙伴近似学习者（PAL）：在多代理商域中使用明确的合作伙伴建模进行模拟加速学习

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Mixed cooperative-competitive control scenarios where the interacting partners exhibit individual goals are very challenging for reinforcement learning agents. An example of such scenarios is given by human-machine interaction. In order to contribute towards intuitive human-machine collaboration, this work focuses on problems in the continuous state and control domain and prohibits explicit communication. More precisely, the agents do not know the others' goals or control laws but only sense their control inputs retrospectively. The proposed framework combines a partner model learned from online data with a reinforcement learning agent that is trained in a simulated environment including the partner model. This procedure overcomes drawbacks of independent learners and benefits from a reduced amount of real world data required for reinforcement learning—an aspect that is vital in the human-machine context. Experimental results reveal that the method learns fast due to the simulated environment and adapts to the constantly changing partner due of the partner model.
机译：对于强化学习代理而言，互动合作伙伴展现出各自目标的混合合作竞争控制场景是非常具有挑战性的。人机交互给出了这种情况的一个例子。为了促进直观的人机协作，这项工作着眼于连续状态和控制领域中的问题，并禁止进行明确的交流。更准确地说，代理人不知道其他人的目标或控制律，而只能追溯地感觉到他们的控制输入。提出的框架将从在线数据中学习到的合作伙伴模型与在包括合作伙伴模型的模拟环境中训练的强化学习代理相结合。此过程克服了独立学习者的弊端，并从减少的强化学习所需的真实世界数据中受益（这在人机环境中至关重要）。实验结果表明，该方法在模拟环境下学习很快，并且由于伙伴模型而适应不断变化的伙伴。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号