We introduce RLang, a domain-specific language (DSL) for communicating domain knowledge to an RL agent. Unlike existing RL DSLs that ground to \textit{single} elements of a decision-making formalism (e.g., the reward function or policy), RLang can specify information about every element of a Markov decision process. We define precise syntax and grounding semantics for RLang, and provide a parser that grounds RLang programs to an algorithm-agnostic \textit{partial} world model and policy that can be exploited by an RL agent. We provide a series of example RLang programs demonstrating how different RL methods can exploit the resulting knowledge, encompassing model-free and model-based tabular algorithms, policy gradient and value-based methods, hierarchical approaches, and deep methods.
@article{arxiv.2208.06448,
title = {RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents},
author = {Rafael Rodriguez-Sanchez and Benjamin A. Spiegel and Jennifer Wang and Roma Patel and Stefanie Tellex and George Konidaris},
journal= {arXiv preprint arXiv:2208.06448},
year = {2023}
}