Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning
A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL). To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice. Unlike previous research, that focuses on optimal...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8698221/ |