在机器人任务中探索尖端神经网络以进行深度强化学习
Luca Zanatta1, Francesco Barchi2, Simone Manoni2
1Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", Università di Bologna, 40126, Bologna, Italy. luca.zanatta3@unibo.it.
Scientific reports
|December 27, 2024
概括
尖端神经网络 (SNN) 在深度强化学习 (DRL) 任务中表现有前途. 我们的框架可以实现高效的SNN配置,并证明最佳的SNN比ANN具有更少的层次,从而提高机器人控制任务的性能.
科学领域:
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 尖端神经网络 (SNN) 模仿哺乳动物的大脑,使用尖端神经元和普通微分方程来表示动态系统.
- 深度强化学习 (DRL),特别是近距离政策优化 (PPO),对于机器人培训至关重要,因为与离线数据集的挑战.
- 通过捕捉输入动态,SNN为具有时间复杂性的DRL任务提供了潜在的潜力.
研究的目的:
- 调查SNN在深度强化学习 (DRL) 机器人任务中的有效性.
- 在Isaac Gym模拟器中开发和使用一种用于训练SNN与PPO的新型框架.
- 为了在各种网络配置中比较SNN和人工神经网络 (ANN) 的性能.
主要方法:
- 在Isaac Gym模拟器中使用skrl库开发了一个用于训练SNNs与PPO的新框架.
- 对DRL机器人任务的SNN配置进行了广泛的探索.
- 比较SNN和ANN,分析网络深度 (层) 和宽度 (神经元).
主要成果:
- 确定DRL任务的最佳SNN拓通常与ANN相比具有更少的层次.
- 观察到,最先进的SNN架构在复杂的RL任务中有效地利用更深层,如Ant.
- 与之前的SNN方法相比,使用确定最佳的SNN拓,在Ant-v4基准上实现了4.4倍的性能改善.
结论:
- 对于DRL任务,SNN是有效的,而较浅的网络架构是最佳的.
- 开发的基于Isaac Gym的框架为DRL提供了高效的SNN配置和比较.
- 这些发现表明,优化的SNN在具有挑战性的机器人控制场景中,比现有的SNN方法具有显著的性能优势.
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