Adaptive Noise Exploration for Neural Contextual Multi-Armed Bandits
In contextual multi-armed bandits, the relationship between contextual information and rewards is typically unknown, complicating the trade-off between exploration and exploitation. A common approach to address this challenge is the Upper Confidence Bound (UCB) method, which constructs confidence in...
Saved in:
| Main Authors: | Chi Wang, Lin Shi, Junru Luo |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/18/2/56 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Model-based exploration is measurable across tasks but not linked to personality and psychiatric assessments
by: Kristin Witte, et al.
Published: (2025-07-01) -
Fair Probabilistic Multi-Armed Bandit With Applications to Network Optimization
by: Zhiwu Guo, et al.
Published: (2024-01-01) -
Gaussian Process with Vine Copula-Based Context Modeling for Contextual Multi-Armed Bandits
by: Jong-Min Kim
Published: (2025-06-01) -
Multi-Dimensional Arms for Combinatorial Multi-Armed Bandit
by: Qi Li, et al.
Published: (2025-01-01) -
Selective Reviews of Bandit Problems in AI via a Statistical View
by: Pengjie Zhou, et al.
Published: (2025-02-01)