通过多维过渡重点融合重点重复在路径规划中重复优先经验
Nuo Cheng1, Peng Wang1,2, Guangyuan Zhang1
1School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, Shandong, China.
Frontiers in neurorobotics
|November 30, 2023
概括
这项研究引入了一种新的方法,用于在智能机器人路径规划的深度决定性政策梯度 (DDPG) 算法中优先考虑经验过渡. 该方法通过智能抽样关键经验来提高培训效率和成功率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 智能机器人路径规划的深度决定性政策梯度 (DDPG) 算法面临随机体验重复的挑战,导致训练效率低下和成功率降低.
- 当前的方法往往过度强调不那么关键的经验过渡,减缓了融合,并降低了路径规划的有效性.
研究的目的:
- 为基于DDPG的机器人路径规划开发一种改进的经验过渡优先级方法.
- 通过准确评估经验过渡的价值来增强培训过程.
- 加速算法融合,提高路径规划的成功率.
主要方法:
- 根据即时奖励,时间差错 (TD-error) 和Actor网络损失计算经验过渡优先级.
- 使用信息作为权重来确定最终的经验过渡优先级,将这些优先级合并.
- 引入了积极经验过渡的适应性调整,并从优先级中导出了抽样概率.
主要成果:
- 与优先体验重复 (PER) 算法相比,提出的方法证明了较短的测试时间.
- 实验结果显示,与障碍物碰撞的次数减少,这表明更有效的路径规划.
- 确定的经验过渡优先事项准确地反映了过渡对培训的重要性.
结论:
- 新的优先级方法显著提高了DDPG算法中过渡重复的利用率.
- 实现了更好的融合速度和更高的路径规划成功率.
- 这种方法为训练复杂环境中的智能机器人提供了更有效的策略.
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