点击现场:在随机环境中进行层次强化学习的可达性指导子目标生成
Bo Chen1, Quan Yuan1, Guiyang Luo1
1Beijing University of Posts and Telecommunications china, China.
概括
层次增强学习代理现在可以在复杂的随机环境中生成更好的子目标. 我们的方法通过模拟低级执行能力来提高目标实现和训练速度.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 层次增强学习 (HRL) 用次目标来进行稀疏的奖励任务.
- 在HRL中低级执行不稳定性阻碍了高级规划,特别是在随机环境中.
研究的目的:
- 为HRL.开发一个可达性引导的次目标生成方法.
- 通过准确地表示低级执行能力来增强高级规划.
主要方法:
- 从理论上分析过渡距离到子目标,证明正常分布.
- 过渡距离模型的衍生平均值和方差.
- 将距离模型集成到高级值预测网络中.
主要成果:
- 拟议的方法准确地代表了低级执行能力.
- 对于随机环境,增强了子目标生成.
- 实验结果显示,完成率更高,收速度更快.
结论:
- 以可达性为导向的方法在随机设置中改善了HRL性能.
- 对低级执行的准确建模对于有效的高级次目标生成至关重要.
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