  We present PDDLGym, a framework that automatically constructs OpenAI Gym
environments from PDDL domains and problems. Observations and actions in
PDDLGym are relational, making the framework particularly well-suited for
research in relational reinforcement learning and relational sequential
decision-making. PDDLGym is also useful as a generic framework for rapidly
building numerous, diverse benchmarks from a concise and familiar specification
language. We discuss design decisions and implementation details, and also
illustrate empirical variations between the 20 built-in environments in terms
of planning and model-learning difficulty. We hope that PDDLGym will facilitate
bridge-building between the reinforcement learning community (from which Gym
emerged) and the AI planning community (which produced PDDL). We look forward
to gathering feedback from all those interested and expanding the set of
available environments and features accordingly. Code:
https://github.com/tomsilver/pddlgym
