论文标题
Blue River控件：硬件上的增强学习控制系统的工具包
Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardware
论文作者
论文摘要
我们在Quanser的硬件软件开发套件（HIL SDK）周围提供了一个简单的硬件包装器，以便于开发新的Quanser硬件。要连接到硬件，我们使用用Cython编写的模块。内部QuanserWrapper类处理与硬件交互的大多数困难方面，包括计时（使用硬件计时器），并确保发送到硬件的数据是安全且正确的，在此安全性与安全的操作电压和指定硬件的电流相对应。通过OpenAi Gym和DeepMind Control Suite（例如，培训和测试工具），加强学习（RL）最近的大部分成功都成为可能。不幸的是，用于快速测试和转移高频RL算法从模拟到真实硬件环境的工具主要不存在。我们提供蓝河控件，该工具允许在现实世界硬件上训练和测试加强学习算法。它具有基于OpenAI健身房的简单界面，该界面直接用于模拟和硬件。我们将Quanser的Qube Servo2-USB平台（一种不足的旋转摆布）作为初始测试设备。我们还提供了简化其他硬件培训RL算法的工具。包括来自经典控制器和预验证的RL代理的几个基线，以比较跨任务的性能。 Blue River Controls可在此https URL上获得：https：//github.com/bluerivertech/quanser-openai-driver
We provide a simple hardware wrapper around the Quanser's hardware-in-the-loop software development kit (HIL SDK) to allow for easy development of new Quanser hardware. To connect to the hardware we use a module written in Cython. The internal QuanserWrapper class handles most of the difficult aspects of interacting with hardware, including the timing (using a hardware timer), and ensuring the data sent to hardware is safe and correct, where safety corresponds to safe operating voltage and current for the specified hardware. Much of the recent success of Reinforcement learning (RL) has been made possible with training and testing tools like OpenAI Gym and Deepmind Control Suite. Unfortunately, tools for quickly testing and transferring high-frequency RL algorithms from simulation to real hardware environment remain mostly absent. We present Blue River Controls, a tool that allows to train and test reinforcement learning algorithms on real-world hardware. It features a simple interface based on OpenAI Gym, that works directly on both simulation and hardware. We use Quanser's Qube Servo2-USB platform, an underactuated rotary pendulum as an initial testing device. We also provide tools to simplify training RL algorithms on other hardware. Several baselines, from both classical controllers and pretrained RL agents are included to compare performance across tasks. Blue River Controls is available at this https URL: https://github.com/BlueRiverTech/quanser-openai-driver
