论文标题
生物力学姿势稳定通过多政策深钢筋学习剂的迭代训练
Biomechanic Posture Stabilisation via Iterative Training of Multi-policy Deep Reinforcement Learning Agents
论文作者
论文摘要
直到我们成为老年人时，我们才意识到我们要维持一个简单的站立姿势是理所当然的。实时观察人脑锻炼的控制幅度真是令人着迷，以激活和停用下半身的肌肉并解决一个多连锁3D 3D反向的摆问题，以保持稳定的站立姿势。当训练人工智能（AI）代理以维持数字肌肉骨骼化身的常规姿势时，由于错误传播问题，这种认识更加明显。在这项工作中，我们通过引入深入强化学习的迭代培训程序来解决错误传播问题，该过程使代理商可以学习一套有限的动作以及如何在它们之间进行协调以实现稳定的站立姿势。拟议的训练方法使代理使用传统训练方法将站立持续时间从4秒钟增加到348秒，使用拟议的方法。提出的训练方法使代理商可以概括和适应近108秒的感知和致动噪声。
It is not until we become senior citizens do we recognise how much we took maintaining a simple standing posture for granted. It is truly fascinating to observe the magnitude of control the human brain exercises, in real time, to activate and deactivate the lower body muscles and solve a multi-link 3D inverted pendulum problem in order to maintain a stable standing posture. This realisation is even more apparent when training an artificial intelligence (AI) agent to maintain a standing posture of a digital musculoskeletal avatar due to the error propagation problem. In this work we address the error propagation problem by introducing an iterative training procedure for deep reinforcement learning which allows the agent to learn a finite set of actions and how to coordinate between them in order to achieve a stable standing posture. The proposed training approach allowed the agent to increase standing duration from 4 seconds using the traditional training method to 348 seconds using the proposed method. The proposed training method allowed the agent to generalise and accommodate perception and actuation noise for almost 108 seconds.
