Name
Abstract | ||
|---|---|---|
Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled approaches to share and transfer knowledge among similar tasks. We present a novel approach for learning a nonlinear feedback policy that generalizes across multiple tasks. The key idea is to define a parametrized policy as a function of both the state and the task, which allows learning a single policy that generalizes across multiple known and unknown tasks. Applications of our novel approach to reinforcement and imitation learning in real-robot experiments are shown. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Multi-task learning,Computer science,Artificial intelligence,Error-driven learning,Reinforcement,Imitation learning,Robotics,Machine learning,Reinforcement learning |
DocType | Volume | Citations |
Journal | abs/1307.0813 | 1 |
PageRank | References | Authors |
0.43 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marc Peter Deisenroth | 1 | 1095 | 64.71 |
| Peter Englert | 2 | 16 | 1.96 |
| Jan Peters | 3 | 3553 | 264.28 |
| Dieter Fox | 4 | 12306 | 1289.74 |