An empowerment-based solution to robotic manipulation tasks with sparse rewards
Abstract In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues reinforcement learning algorithms face whe...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer US
2023
|
| Online Access: | https://hdl.handle.net/1721.1/151079 |