AlphaRouter: Bridging the Gap Between Reinforcement Learning and Optimization for Vehicle Routing with Monte Carlo Tree Searches
Deep reinforcement learning (DRL) as a routing problem solver has shown promising results in recent studies. However, an inherent gap exists between computationally driven DRL and optimization-based heuristics. While a DRL algorithm for a certain problem is able to solve several similar problem inst...
Saved in:
| Main Authors: | Won-Jun Kim, Junho Jeong, Taeyeong Kim, Kichun Lee |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/27/3/251 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
MCTS-NC: A thorough GPU parallelization of Monte Carlo Tree Search implemented in Python via numba.cuda
by: Przemysław Klęsk
Published: (2025-05-01) -
Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing
by: Aigerim Bogyrbayeva, et al.
Published: (2025-02-01) -
Internet intelligent routing architecture and algorithm
by: Fei GUI, et al.
Published: (2020-10-01) -
Energy-Efficient Multi-Hop Routing Protocol for Tag-to-Tag Communication in Passive RFID Networks Using Reinforcement Learning
by: Guowei Guo, et al.
Published: (2025-01-01) -
A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems
by: Dapeng Yan, et al.
Published: (2025-04-01)