A robust approach for multi-agent natural resource allocation based on stochastic optimization algorithms
By Nikos Barbalios, Panagiotis Tzionas. 2014
17 citations recorded by the source corpus.
Subfields: Building and Construction, Electrical and Electronic Engineering, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
In Management Science and Operations Research
- Shayegan Omidshafiei
- Daan Bloembergen
- Jonathan P. How
- Karl Tuyls
- Rahul Savani
- Gregory Palmer
- Jason Pazis
- Chris Amato
Citations in the corpus
- Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability (2017, methodology): “Hysteretic Q-learning has enjoyed a strong empirical track record in fully-observable MARL (Xu et al., 2012; Matignon et al., 2012; Barbalios & Tzionas, 2014), exhibiting similar performance as more complex approaches.”
- Lenient Multi-Agent Deep Reinforcement Learning (2017, background): “Hysteretic Q-learning is a form of optimistic learning with a strong empirical track record in fully-observable MA-RL [2, 18, 37].”