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在物理模拟器中的自学虚拟生物:关于它们视觉系统的最佳分辨率,神经系统的架构和问题的计算复杂性

M S Zenin¹, A P Devyaterikov², A Yu Palyanov³

¹Novosibirsk State University, Novosibirsk, Russia.

Vavilovskii zhurnal genetiki i selektsii

|January 15, 2026

概括

研究人员在3D环境中模拟了具有视觉能力的虚拟生物. 最佳的视觉系统分辨率平衡了复杂性和任务成功,高维度减缓了强化学习 (RL) 中的学习.

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:

视觉对于生物的行为至关重要,从简单的昆虫到复杂的哺乳动物.
虚拟生物的建模有助于理解感觉运动集成和认知限制.
复杂的视觉系统需要大量的大脑资源,需要高效的处理.

研究的目的:

在3D环境中与视觉系统实现和研究虚拟生物模型.
探索视觉系统参数对强化学习成果的影响.
分析传感输入维度和学习效率之间的权衡.

主要方法:

利用Unity ML-Agents进行虚拟生物的高性能模拟.
提出了一种分层的控制架构,将感知/决策与运动分开.
使用近接政策优化 (PPO) 算法进行强化学习实验.
进行了数字实验,改变视觉分辨率和代理架构.

主要成果:

确定了最佳的视觉分辨率范围,平衡计算成本和任务成功.
证明过度的感官输入或动作空间维度会减缓学习.
系统性能分析揭示了大规模模拟中的关键瓶.

结论:

生物灵感的人工制剂可以从优化的视觉系统参数中受益.
神经进化方法可以增强代理架构的适应性.
该研究提供了对人工系统中感知-动作循环和认知过程的见解.

关键词:

这就是PPO PPO PPO.单元ML-Agents 是一个单元.计算复杂性计算复杂性计算建模计算建模神经网络的神经网络的神经网络强化学习是一种强化学习.一个模拟器模拟器.虚拟生物是一个虚拟的生物体.视觉系统视觉系统

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