论文标题
pyrddlgym：从rddl到健身环境
pyRDDLGym: From RDDL to Gym Environments
论文作者
论文摘要
我们提出了Pyrddlgym，这是一个从RDDL Declerative描述中为OpenAI健身环境自动产生的Python框架。 RDDL中变量的离散时间步长的演变由条件概率函数描述，该概率函数自然适合健身步骤方案。此外，由于RDDL是一个提起的描述，因此对环境的修改和扩展，以支持多个实体和不同的配置变得琐碎，而不是容易出现错误的过程。我们希望，由于RDDL的独特表现力，Pyrddlgym将通过轻松而快速的基准发展来成为增强学习社区的新风。通过在RDDL描述中提供明确访问模型的访问，Pyrddlgym还可以促进有关混合方法的研究，以在利用模型知识的同时从相互作用中学习。我们介绍了pyrddlgym的设计和内置示例，以及对RDDL语言的添加添加到框架中。
We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description. The discrete time step evolution of variables in RDDL is described by conditional probability functions, which fits naturally into the Gym step scheme. Furthermore, since RDDL is a lifted description, the modification and scaling up of environments to support multiple entities and different configurations becomes trivial rather than a tedious process prone to errors. We hope that pyRDDLGym will serve as a new wind in the reinforcement learning community by enabling easy and rapid development of benchmarks due to the unique expressive power of RDDL. By providing explicit access to the model in the RDDL description, pyRDDLGym can also facilitate research on hybrid approaches for learning from interaction while leveraging model knowledge. We present the design and built-in examples of pyRDDLGym, and the additions made to the RDDL language that were incorporated into the framework.
