基于深度决定性的政策梯度的动态环境中移动机器人的自动驾驶:奖励塑造和回顾经验重播
Minjae Park1, Chaneun Park2, Nam Kyu Kwon1
1Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Biomimetics (Basel, Switzerland)
|January 22, 2024
概括
本研究介绍了一种强化学习方法,用于移动机器人在充满障碍的动态环境中进行导航. 该方法使用奖励塑造和事后经验重复来实现无碰撞的自动驾驶.
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:
- 在动态环境中自主导航对移动机器人构成重大挑战.
- 传统的方法往往难以实时避开障碍物和优化路径规划.
研究的目的:
- 开发一个自主移动机器人驾驶的端到端强化学习方法.
- 在充满障碍的动态环境中提高航行效率和安全性.
主要方法:
- 提出了一种基于强化学习 (RL) 的端到端学习方法.
- 集成的多功能奖励塑造为目标导向的指导.
- 利用后见经验重播来克服稀疏的奖励挑战.
主要成果:
- 提出的方法证明了有效的自动驾驶能力.
- 在模拟和现实测试中,在没有碰撞的情况下成功实现了导航.
- 在五种场景中进行的比较实验验证了该技术的性能.
结论:
- 结合的强化学习技术使得最佳的政策发现能够实现无碰撞的航行.
- 该方法在具有障碍的动态环境中是稳健有效的.
- 这种方法推进了移动机器人的自动驾驶系统.
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