Name
Abstract | ||
|---|---|---|
We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oord et al., 2017b) for forward planning with MCTS. In order to test this method, we introduce a new environment featuring varying difficulty levels, along with moving goals and obstacles. The combination of high-quality frame generation and classical planning approaches nearly matches true environment performance for our task, demonstrating the usefulness of this method for model-based planning in dynamic environments. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1811.10097 | 0 | 0.34 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johanna Hansen | 1 | 1 | 1.07 |
| kyle kastner | 2 | 68 | 5.24 |
| Aaron C. Courville | 3 | 6671 | 348.46 |
| Gregory Dudek | 4 | 2163 | 255.48 |