视觉强化学习概括的多域观测之间的双向过渡一致性
Xiaobo Hu1, Youfang Lin1, Jinwen Wang1
1Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
概括
多域双向转换 (MDBT) 模型通过创建处理视觉干扰的强大表示来增强视觉增强学习. 这种方法可以提高各种控制任务和机器人操纵基准的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 视觉增强学习 (RL) 在视觉干扰下与可概括的表示作斗争.
- 现有的方法往往侧重于视觉一致性或精确的基于模型的转换,限制了强度.
研究的目的:
- 引入多域双向转换 (MDBT) 模型,用于学习视觉RL中的强大和可转移的表示.
- 用高维图像观测和各种视觉干扰来应对政策学习的挑战.
主要方法:
- MDBT结合了具有视觉扰乱和内核区域的多域观测.
- 数据转换模块多样化了观测,而双向转换模块提取了与任务相关的动态.
- 一个一致性目标确保在学习的表示中消除噪音和任务相关性.
主要成果:
- 在DeepMind控制套件上,MDBT实现了最先进的性能,成功率提高了2.3%.
- 在机器人操纵任务 (高达124.1%) 和CARLA驾驶模拟 (高达23.2%) 中观察到显著的改进.
- 该模型在恶劣天气和照明干扰下表现出增强的稳定性.
结论:
- MDBT有效地学习可视化增强学习的强大和可转移的表示.
- 拟议的模型克服了先前处理视觉干扰的方法的局限性.
- 对于需要可靠视觉感知的现实应用,MDBT显示出强大的潜力.
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