Name
Abstract | ||
|---|---|---|
To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction. Motivated by the absence of a model satisfying all these requirements, we propose TD-VAE, a generative sequence model that learns representations containing explicit beliefs about states several steps into the future, and that can be rolled out directly without single-step transitions. TD-VAE is trained on pairs of temporally separated time points, using an analogue of temporal difference learning used in reinforcement learning. |
Year | Venue | Field |
|---|---|---|
2019 | ICLR | Temporal difference learning,Autoencoder,Pattern recognition,Computer science,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karol Gregor | 1 | 1173 | 72.53 |
| George Papamakarios | 2 | 25 | 5.25 |
| Frederic Besse | 3 | 100 | 5.17 |
| Lars Buesing | 4 | 248 | 16.50 |
| Theophane Weber | 5 | 159 | 16.79 |