论文标题
Rlang：一种描述部分世界知识以加强学习代理商的声明性语言
RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
论文作者
论文摘要
我们介绍了Rlang，这是一种特定于领域的语言（DSL），用于将域知识传达给RL代理。与基于决策形式主义的\ textit {single {single {single {single {单}元素的现有RL DSL不同（例如，奖励功能或政策），Rlang可以指定有关马尔可夫决策过程中每个元素的信息。我们为Rlang定义了精确的语法和基础语义，并提供了一个解析器，将Rlang编程的基础为算法 - agnoftic \ textit {partial}世界模型和策略，该模型和策略可以由RL代理利用。我们提供了一系列示例Rlang程序，演示了不同的RL方法如何利用所得的知识，包括无模型和基于模型的表格算法，策略梯度和基于价值的方法，层次结构方法以及深度方法。
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.
