Deep deterministic policy gradient algorithm based on dung beetle optimization and priority experience replay mechanism
Abstract Reinforcement learning algorithms that handle continuous action spaces have the problem of slow convergence and local optimality. Hence, we propose a deep deterministic policy gradient algorithm based on the dung beetle optimization algorithm (DBOP–DDPG) and priority experience replay mecha...
Saved in:
| Main Authors: | Hengwei Zhu, Chuiting Rong, Haorui Liu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-99213-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhanced deep deterministic policy gradient algorithm
by: Jianping CHEN, et al.
Published: (2018-11-01) -
Double Critics and Double Actors Deep Deterministic Policy Gradient for Mobile Robot Navigation Using Adaptive Parameter Space Noise and Parallel Experience Replay
by: Wenjie Hu, et al.
Published: (2024-01-01) -
Priority Control of Intelligent Connected Dedicated Bus Corridor Based on Deep Deterministic Policy Gradient
by: Chunlin Shang, et al.
Published: (2025-08-01) -
Deep deterministic policy gradient-based energy efficiency optimization algorithm for CR-NOMA
by: ZHANG Yun
Published: (2024-05-01) -
Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning
by: Yana Yang, et al.
Published: (2024-12-01)