Path Planning for Robots Combined with Zero-Shot and Hierarchical Reinforcement Learning in Novel Environments
Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In unseen or complex environmental information, agents often perform sub-optimally and require more training time. In response to these cha...
Saved in:
| Main Authors: | Liwei Mei, Pengjie Xu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Actuators |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-0825/13/11/458 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Focusing on Valid Search Space in Open-World Compositional Zero-Shot Learning by Leveraging Misleading Answers
by: Soohyeong Kim, et al.
Published: (2024-01-01) -
Zero-Shot Food Image Detection Based on Transformer
by: Jingru SONG, et al.
Published: (2024-11-01) -
Multi-Agent Hierarchical Graph Attention Actor–Critic Reinforcement Learning
by: Tongyue Li, et al.
Published: (2024-12-01) -
PLZero: placeholder based approach to generalized zero-shot learning for multi-label recognition in chest radiographs
by: Chengrong Yang, et al.
Published: (2025-01-01) -
Enhanced Partial Fourier MRI With Zero-Shot Deep Untrained Priors
by: So Hyun Kang, et al.
Published: (2024-01-01)