Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces
Machine Learning
2020-02-11 v3
Abstract
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.
Cite
@article{arxiv.1905.09700,
title = {Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces},
author = {Chen Tessler and Tom Zahavy and Deborah Cohen and Daniel J. Mankowitz and Shie Mannor},
journal= {arXiv preprint arXiv:1905.09700},
year = {2020}
}
Comments
Under review at IJCAI 2020