Name
Abstract | ||
|---|---|---|
Recent advancements in Bayesian likelihood-free inference enables a probabilistic treatment for the problem of estimating simulation parameters and their uncertainty given sequences of observations. Domain randomization can be performed much more effectively when a posterior distribution provides the correct uncertainty over parameters in a simulated environment. In this paper, we study the integration of simulation parameter inference with both model-free reinforcement learning and model-based control in a novel sequential algorithm that alternates between learning a better estimation of parameters and improving the controller. This approach exploits the interdependence between the two problems to generate computational efficiencies and improved reliability when a black-box simulator is available. Experimental results suggest that both control strategies have better performance when compared to traditional domain randomization methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IROS45743.2020.9341401 | IROS |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Possas Rafael | 1 | 3 | 0.78 |
| Lucas Barcelos | 2 | 1 | 0.34 |
| Rafael Oliveira | 3 | 3 | 3.15 |
| Dieter Fox | 4 | 12306 | 1289.74 |
| Fabio Ramos | 5 | 10 | 3.67 |