Reinforcement learning (RL) is one of the most active fields of AI research. Despite the interest demonstrated by the research community in reinforcement learning, the development methodology still lags behind, with a severe lack of standard APIs to foster the development of RL applications. OpenAI Gym is probably the most used environment to develop RL applications and simulations, but most of the abstractions proposed in such a framework are still assuming a semi-structured methodology. This is particularly relevant for agent-based models whose purpose is to analyse adaptive behaviour displayed by self-learning agents in the simulation. In order to bridge this gap, we present a workflow and tools for the decoupled development and maintenance of multi-purpose agent-based models and derived single-purpose reinforcement learning environments, enabling the researcher to swap out environments with ones representing different perspectives or different reward models, all while keeping the underlying domain model intact and separate. The Sim-Env Python library generates OpenAI-Gym-compatible reinforcement learning environments that use existing or purposely created domain models as their simulation back-ends. Its design emphasizes ease-of-use, modularity and code separation.

中文翻译：

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Sim-Env：将OpenAI体育馆环境与仿真模型分离

强化学习（RL）是AI研究中最活跃的领域之一。尽管研究团体表现出了对强化学习的兴趣，但是开发方法仍然滞后，严重缺乏促进RL应用程序开发的标准API。OpenAI Gym可能是开发RL应用程序和模拟的最常用的环境，但是在这样的框架中提出的大多数抽象仍然采用半结构化方法。这对于基于代理的模型尤其重要，该模型的目的是分析自学习代理在模拟中显示的自适应行为。为了弥合这种差距，我们提出了一种工作流程和工具，用于基于代理的多用途模型和派生的单用途强化学习环境的分离开发和维护，使研究人员可以用代表不同观点或不同奖励模型的环境交换环境，同时保持基础领域模型的完整性和独立性。Sim-Env Python库生成与OpenAI-Gym兼容的增强学习环境，该环境使用现有或专门创建的域模型作为其模拟后端。它的设计强调易用性，模块化和代码分离。