Leveraging Transfer Learning in Deep Reinforcement Learning for Solving Combinatorial Optimization Problems Under Uncertainty
In recent years, addressing the inherent uncertainties within Combinatorial Optimization Problems (COPs) reveals the limitations of traditional optimization methods. Although these methods are often effective in deterministic settings, they may lack flexibility and adaptability to navigate the uncer...
Saved in:
| Main Author: | Fatima Ezzahra Achamrah |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10766597/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing
by: Aigerim Bogyrbayeva, et al.
Published: (2025-02-01) -
Deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertainty
by: Shirali Kadyrov, et al.
Published: (2025-12-01) -
AlphaRouter: Bridging the Gap Between Reinforcement Learning and Optimization for Vehicle Routing with Monte Carlo Tree Searches
by: Won-Jun Kim, et al.
Published: (2025-02-01) -
A Combined Diffusion Model and Reinforcement Learning Approach for Solving the Vehicle Routing Problem With Multiple Soft Time Windows
by: Yu Qiao, et al.
Published: (2025-01-01) -
Solving a Vehicle Routing Problem under Uncertainty by a Differential Evolution Algorithm
by: Alireza Salamatbakhsh, et al.
Published: (2021-02-01)