Name
Abstract | ||
|---|---|---|
In this paper, we present a Bayesian view on model-based reinforcement learning. We use expert knowledge to impose structure on the transition model and present an efficient learning scheme based on variational inference. This scheme is applied to a heteroskedastic and bimodal benchmark problem on which we compare our results to NFQ and show how our approach yields human-interpretable insight about the underlying dynamics while also increasing data-efficiency. |
Year | Venue | DocType |
|---|---|---|
2019 | ESANN | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaiser, Markus | 1 | 1 | 1.40 |
| Clemens Otte | 2 | 5 | 4.53 |
| Thomas A. Runkler | 3 | 345 | 47.43 |
| carl henrik ek | 4 | 327 | 30.76 |