Name
Title | ||
|---|---|---|
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces. |
Abstract | ||
|---|---|---|
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. We propose 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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1905.09700 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Tessler | 1 | 0 | 1.35 |
| Tom Zahavy | 2 | 58 | 8.81 |
| Deborah J. Cohen | 3 | 4 | 3.13 |
| Daniel J. Mankowitz | 4 | 29 | 8.05 |
| Shie Mannor | 5 | 3340 | 285.45 |