Name
Abstract | ||
|---|---|---|
State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is a challenging open problem. We introduce a method that learns state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state variables. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods. |
Year | Venue | Keywords |
|---|---|---|
2019 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019) | mutual information,intelligent agents |
Field | DocType | Volume |
State representation,Computer science,Artificial intelligence,Machine learning | Journal | 32 |
ISSN | Citations | PageRank |
1049-5258 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ankesh Anand | 1 | 0 | 1.35 |
| Evan Racah | 2 | 54 | 5.35 |
| Sherjil Ozair | 3 | 408 | 15.62 |
| Yoshua Bengio | 4 | 42677 | 3039.83 |
| Marc-Alexandre Côté | 5 | 0 | 0.34 |
| R Devon Hjelm | 6 | 135 | 13.28 |