论文标题
用于增强学习的眼科生物力学环境
An ocular biomechanics environment for reinforcement learning
论文作者
论文摘要
增强学习已通过基于生理的生物力学模型应用于人类运动，以增加对这些运动的神经控制的见解。它在假肢和机器人技术的设计中也很有用。在本文中，我们将增强学习的使用扩展到控制眼部生物力学系统以执行扫视，这是最快的眼动系统之一。我们描述了一种眼部环境和一种使用深层确定性政策梯度方法训练的代理，以执行扫视。代理能够以3：5 +/- 1:25度的平均偏差角度匹配所需的眼睛位置。拟议的框架是利用深度强化学习能力来增强我们对眼部生物力学的理解的第一步。
Reinforcement learning has been applied to human movement through physiologically-based biomechanical models to add insights into the neural control of these movements; it is also useful in the design of prosthetics and robotics. In this paper, we extend the use of reinforcement learning into controlling an ocular biomechanical system to perform saccades, which is one of the fastest eye movement systems. We describe an ocular environment and an agent trained using Deep Deterministic Policy Gradients method to perform saccades. The agent was able to match the desired eye position with a mean deviation angle of 3:5+/-1:25 degrees. The proposed framework is a first step towards using the capabilities of deep reinforcement learning to enhance our understanding of ocular biomechanics.
