In a simulated supply chain driven by a fitted demand model, MARL pricing agents earn far higher revenue than rule-based agents while reducing fairness and stability.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
In a simulated supply chain driven by a fitted demand model, MARL pricing agents earn far higher revenue than rule-based agents while reducing fairness and stability.