Name
Abstract | ||
|---|---|---|
We interpret meta-reinforcement learning as the problem of learning how to quickly find a good sampling distribution in a new environment. This interpretation leads to the development of two new meta-reinforcement learning algorithms: E-MAML and E-RL2. Results are presented on a new environment we call `Krazy Worldu0027: a difficult high-dimensional gridworld which is designed to highlight the importance of correctly differentiating through sampling distributions in meta-reinforcement learning. Further results are presented on a set of maze environments. We show E-MAML and E-RL2 deliver better performance than baseline algorithms on both tasks. |
Year | Venue | Keywords |
|---|---|---|
2018 | NeurIPS | a set,learning algorithms,sampling distribution |
Field | DocType | Citations |
Sampling distribution,Computer science,Sampling (statistics),Artificial intelligence,Machine learning,Reinforcement learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| bradly c stadie | 1 | 82 | 6.02 |
| Ge Yang | 2 | 12 | 1.57 |
| Rein Houthooft | 3 | 600 | 21.07 |
| Xi Chen | 4 | 1649 | 54.94 |
| Yan Duan | 5 | 775 | 27.97 |
| Wu, Yuhuai | 6 | 158 | 9.68 |
| Pieter Abbeel | 7 | 6363 | 376.48 |
| Ilya Sutskever | 8 | 25814 | 1120.24 |