论文标题
EasyRl：一个简单且可扩展的增强学习框架
EasyRL: A Simple and Extensible Reinforcement Learning Framework
论文作者
论文摘要
近年来，增强学习（RL）已成为一个流行的研究领域，也成为从事尖端人工智能研究的企业的工具。为此，许多研究人员建立了RL框架，例如Openai Gym和Kerasrl，以便于使用。尽管这些作品在为RL的新手带来的进入障碍方面取得了长足的进步，但我们提出了一个更简单的框架，称为EasyRl，通过提供交互式的图形用户界面供用户训练和评估RL代理。由于它完全是图形的，因此EasyRL不需要编程知识来培训和测试简单的内置RL代理。 EasyRL还支持自定义RL代理和环境，这对于RL研究人员评估和比较其RL模型可能非常有益。
In recent years, Reinforcement Learning (RL), has become a popular field of study as well as a tool for enterprises working on cutting-edge artificial intelligence research. To this end, many researchers have built RL frameworks such as openAI Gym and KerasRL for ease of use. While these works have made great strides towards bringing down the barrier of entry for those new to RL, we propose a much simpler framework called EasyRL, by providing an interactive graphical user interface for users to train and evaluate RL agents. As it is entirely graphical, EasyRL does not require programming knowledge for training and testing simple built-in RL agents. EasyRL also supports custom RL agents and environments, which can be highly beneficial for RL researchers in evaluating and comparing their RL models.
