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RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
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.
On the Relationship Between Structure in Natural Language and Models of Sequential Decision Processes
Human language is distinguished by powerful semantics, rich structure, and incredible flexibility.
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