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Found 3 papers, 2 papers with code
Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks
Neural network models often generalize poorly to mismatched domains or distributions.
BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps
To this end, we propose BabyWalk, a new VLN agent that is learned to navigate by decomposing long instructions into shorter ones (BabySteps) and completing them sequentially.
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees
An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment.
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