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Arxiv
1d
292
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Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
- A study explores how Multi-Agent Reinforcement Learning (MARL) can enhance dynamic pricing strategies in supply chains by considering strategic interactions among market actors.
- The research evaluates three MARL algorithms (MADDPG, MADQN, and QMIX) against static rule-based baselines in a simulated environment using real e-commerce transaction data.
- Results indicate that rule-based agents achieve high fairness and price stability but lack competitive dynamics, while MADQN displays aggressive pricing behavior with high volatility and low fairness.
- MADDPG offers a balanced approach by supporting market competition, maintaining high fairness, and stable pricing, suggesting that MARL introduces emergent strategic behavior in dynamic pricing scenarios.
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