End-to-end reinforcement learning is a promising approach to enable robots to acquire complicated skills. However, this requires numerous samples to be implemented successfully. The issue is that it is often difficult to collect the sufficient number of samples. To accelerate learning in the field of robotics, knowledge gathered from robotics engineering and previously learned tasks must be fully exploited. Specifically, we propose using a sample-efficient curriculum to establish quadrupedal robot control in which the walking and turning tasks are divided into two hierarchical layers, and a robot learns them incrementally from lower to upper layers. To develop such a curriculum, two core components are designed. First the fractal design of neural networks in reservoir computing is aimed at allocating the tasks to be learned to respective modules in fractal networks. This allows mitigating the problem of catastrophic forgetting in neural networks and achieves the capability of continuous learning. The second task includes hierarchical task decomposition according to robotics knowledge for controlling legged robots. Owing to the combination of these two components, the proposed curriculum enables a robot to tune the lower layer even when the upper layer is optimized. As a result of implementing the proposed design, we confirm that a quadrupedal robot in a dynamical simulator succeeds in learning skills hierarchically according to the given curriculum, starting from moving legs and finally, walking/turning, unlike the considered conventional curriculums that are unable to achieve such results.

端到端强化学习是使机器人掌握复杂技能的一种有前途的方法。但是，这需要成功实施大量示例。问题是通常很难收集足够数量的样本。为了加速机器人技术领域的学习，必须充分利用从机器人工程学和先前学习的任务中收集的知识。具体来说，我们建议使用一种示例高效的课程来建立四足机器人控制系统，该系统将步行和转弯任务分为两个层次，然后机器人从下层到上层逐步学习它们。为了开发这样的课程，设计了两个核心组件。首先，储层计算中神经网络的分形设计旨在将要学习的任务分配给分形网络中的各个模块。这可以减轻神经网络中灾难性遗忘的问题，并实现持续学习的能力。第二任务包括根据机器人知识来控制腿式机器人的分层任务分解。由于这两个组成部分的结合，所提出的课程使机器人即使在优化上层时也可以调整下层。实施建议的设计的结果是，我们确认动态模拟器中的四足机器人可以根据给定的课程成功地进行分层学习，从动腿开始，最后是步行/转弯，