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Found 3 papers, 1 papers with code
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.
Guided Policy Search for Parameterized Skills using Adverbs
We present a method for using adverb phrases to adjust skill parameters via learned adverb-skill groundings.
MK-SQuIT: Synthesizing Questions using Iterative Template-filling
The aim of this work is to create a framework for synthetically generating question/query pairs with as little human input as possible.
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