Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces

We propose a computationally efficient algorithm that combines compressed sensing with imitation learning to solve sequential decision making text-based games with combinatorial action spaces. To do so, we derive a variation of the compressed sensing algorithm Orthogonal Matching Pursuit (OMP), that we call IK-OMP, and show that it can recover a bag-of-words from a sum of the individual word embeddings, even in the presence of noise. We incorporate IK-OMP into a supervised imitation learning setting and show that this algorithm, called Sparse Imitation Learning (Sparse-IL), solves the entire text-based game of Zork1 with an action space of approximately 10 million actions using imperfect, noisy demonstrations.