Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces

2019-05-23 · Chen Tessler, Tom Zahavy, Deborah Cohen, Daniel J. Mankowitz, Shie Mannor

We propose a computationally efficient algorithm that combines compressed sensing with imitation learning to solve text-based games with combinatorial action spaces. Specifically, we introduce a new compressed sensing algorithm, named IK-OMP, which can be seen as an extension to the Orthogonal Matching Pursuit (OMP). We incorporate IK-OMP into a supervised imitation learning setting and show that the combined approach (Sparse Imitation Learning, Sparse-IL) solves the entire text-based game of Zork1 with an action space of approximately 10 million actions given both perfect and noisy demonstrations.

📄 PDF Abstract BibTeX arXiv:1905.09700

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compressed sensingDecision MakingImitation Learningtext-based gamesWord Embeddings

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