pyRDDLGym: From RDDL to Gym Environments

Ayal Taitler, Michael Gimelfarb, Jihwan Jeong, Sriram Gopalakrishnan, Martin Mladenov, Xiaotian Liu, Scott Sanner

Keywords: rddl, model-based planning, reinforcement learning, simulation

Abstract: We present pyRDDLGym, a Python framework for the auto-generation of OpenAI Gym environments from RDDL declarative description. The discrete time step evolution of variables in RDDL is described by conditional probability functions, which fit 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 to 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.

Submission Number: 2