An ocular biomechanics environment for reinforcement learning
This work addresses understanding ocular biomechanics for applications in neuroscience and robotics, but it is incremental as it extends existing methods to a new domain.
The paper tackled controlling an ocular biomechanical system to perform saccades using reinforcement learning, achieving a mean deviation angle of 3.5±1.25 degrees in matching desired eye positions.
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.