# pyRDDLGym: From RDDL to Gym Environments

arXiv: https://arxiv.org/abs/2211.05939 · HTML: https://mlsanity.com/paper/2211.05939/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 11 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [PDDLGym: Gym Environments from PDDL Problems](https://mlsanity.com/paper/2002.06432/) (2020)
- [Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach](https://mlsanity.com/paper/2008.00104/) (2020)
- [Scalable Planning with Tensorflow for Hybrid Nonlinear Domains](https://mlsanity.com/paper/1704.07511/) (2017)

## Built on by (most cited first, 8 of 8)

- [Reinforcement learning with combinatorial actions for coupled restless bandits](https://mlsanity.com/paper/2503.01919/) (2025)
- [AUPO -- Abstracted Until Proven Otherwise: A Reward Distribution Based Abstraction Algorithm](https://mlsanity.com/paper/2510.23214/) (2025)
- [Lifted Forward Planning in Relational Factored Markov Decision Processes with Concurrent Actions](https://mlsanity.com/paper/2505.22147/) (2025)
- [HDDLGym: A Tool for Studying Multi-Agent Hierarchical Problems Defined in HDDL with OpenAI Gym](https://mlsanity.com/paper/2505.22597/) (2025)
- [Using Experience Classification for Training Non-Markovian Tasks](https://mlsanity.com/paper/2310.11678/) (2023)
- [Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs](https://mlsanity.com/paper/2401.12243/) (2024)
- [Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints](https://mlsanity.com/paper/2405.03005/) (2024)
- [Model-Driven Policy Optimization in Differentiable Simulators via Stochastic Exploration](https://mlsanity.com/paper/2605.07520/) (2026)

## Abstract (arXiv)

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.