This tutorial reviews a series of reinforcement learning (RL) methods implemented in a reproducing kernel Hilbert space (RKHS) developed to address the challenges imposed on decoder design. RL-based decoders enable the user to learn the prosthesis control through interactions without desired signals and better represent the subject’s goal to complete the task. The numerous actions in complex tasks and nonstationary neural states form a vast and dynamic state–action space, imposing a computational challenge in the decoder to detect the emerging neural patterns as well as quickly establish and adjust the globally optimal policy.

中文翻译：

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再现核希尔伯特空间中的强化学习


本教程回顾了在再现内核希尔伯特空间 (RKHS) 中实现的一系列强化学习 (RL) 方法，这些方法是为了解决解码器设计面临的挑战而开发的。基于强化学习的解码器使用户能够在没有所需信号的情况下通过交互来学习假肢控制，并更好地代表受试者完成任务的目标。复杂任务和非平稳神经状态中的大量动作形成了巨大且动态的状态动作空间，给解码器带来了计算挑战，以检测新兴的神经模式以及快速建立和调整全局最优策略。