论文标题
用于生物学上可可运动控制的差异HEBBIAN框架
A differential Hebbian framework for biologically-plausible motor control
论文作者
论文摘要
在本文中，我们探讨了一种神经控制结构，既有生物学上合理又能够完全自主学习。它由反馈控制器组成，这些反馈控制器通过选择应驱动它们的错误来学会实现所需状态。这种选择是通过差异性HEBBIAN学习规则的家庭进行的，通过与环境的互动，可以学会控制错误对控制信号单调响应的系统。接下来，我们表明，在更一般的情况下，神经增强学习可以与反馈控制器结合在一起，以减少从控制信号中非单调的错误。反馈控制的使用可以降低增强学习问题的复杂性，因为只有控制器可以处理其达到的详细信息，因此只能学习所需的值。这使得该功能变得更简单，有可能学习更复杂的动作。我们使用简单的示例来说明我们的方法，并讨论如何将其扩展到层次结构。
In this paper we explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. It consists of feedback controllers that learn to achieve a desired state by selecting the errors that should drive them. This selection happens through a family of differential Hebbian learning rules that, through interaction with the environment, can learn to control systems where the error responds monotonically to the control signal. We next show that in a more general case, neural reinforcement learning can be coupled with a feedback controller to reduce errors that arise non-monotonically from the control signal. The use of feedback control can reduce the complexity of the reinforcement learning problem, because only a desired value must be learned, with the controller handling the details of how it is reached. This makes the function to be learned simpler, potentially allowing learning of more complex actions. We use simple examples to illustrate our approach, and discuss how it could be extended to hierarchical architectures.
