Name
Title | ||
|---|---|---|
Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes. |
Abstract | ||
|---|---|---|
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings. Our new framework correctly models the joint uncertainty in the latent parameters and the state space. We also replace the original Gaussian Process-based model with a Bayesian Neural Network, enabling more scalable inference. Thus, we expand the scope of the HiP-MDP to applications with higher dimensions and more complex dynamics. |
Year | Venue | DocType |
|---|---|---|
2017 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1706.06544 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taylor Killian | 1 | 1 | 3.75 |
| Samuel Daulton | 2 | 0 | 0.34 |
| George Konidaris | 3 | 801 | 59.30 |
| Finale Doshi-Velez | 4 | 0 | 1.35 |