Name
Title | ||
|---|---|---|
Multifidelity Reinforcement Learning with Gaussian Processes: Model-Based and Model-Free Algorithms |
Abstract | ||
|---|---|---|
We study the problem of reinforcement learning (RL) using as few real-world samples as possible. A naive application of RL can be inefficient in large and continuous-state spaces. We present two versions of multifidelity RL (MFRL), model based and model free, that leverage Gaussian processes (GPs) to learn the optimal policy in a real-world environment. In the MFRL framework, an agent uses multipl... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MRA.2020.2977971 | IEEE Robotics & Automation Magazine |
Keywords | DocType | Volume |
Robots,Prediction algorithms,Approximation algorithms,Automation,Training,Planning | Journal | 27 |
Issue | ISSN | Citations |
2 | 1070-9932 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Varun Suryan | 1 | 0 | 0.34 |
| Nahush Gondhalekar | 2 | 0 | 0.34 |
| Pratap Tokekar | 3 | 213 | 29.34 |