RLang: A Declarative Language for Expression Prior Knowledge for Reinforcement Learning
Published in arXiv, 2022
Communicating useful background knowledge to reinforcement learning (RL) agents is an important and effective method for accelerating learning. We introduce RLang, a domain-specific language (DSL) for communicating domain knowledge to an RL agent. Unlike other existing DSLs proposed by the RL community that ground to single elements of a decision-making formalism (e.g., the reward function or policy function), 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 implementation that grounds RLang programs to an algorithm-agnostic partial world model and policy that can be exploited by an RL agent. We provide a series of example RLang programs, and demonstrate how different RL methods can exploit the resulting knowledge, including model-free and model-based tabular algorithms, hierarchical approaches, and deep RL algorithms (including both policy gradient and value-based methods).
Recommended citation: R Rodriguez-Sanchez, B Spiegel, J Wang, R Patel, S Tellex, G Konidaris. arXiv preprint arXiv:2208.06448 https://arxiv.org/abs/2208.06448