基于独立学习者与相对经验回放的算法定价
Algorithmic pricing with independent learners and relative experience replay
AI总结：
该研究针对无限重复定价博弈中独立强化学习者的算法合谋问题，引入结合相对表现的经验回放技术，发现不同表现容忍度的智能体收敛结果不同，且该方法可缓解过拟合，其效果受智能体数量和算法选择影响。
摘要：
在无限重复的一般和定价博弈中，独立强化学习者可能在无任何通信的情况下表现出合谋行为，引发了对算法合谋的担忧。为更好地理解学习动力学，我们利用经验回放（ER）技术引入了智能体在竞争者中的相对表现（RP）。实验结果表明，RP考量对长期结果起关键作用：厌恶表现不佳的智能体会收敛到Bertrand-Nash均衡，而对表现不佳更容忍的智能体倾向于收取超竞争价格。该发现还有助于缓解独立Q学习中的过拟合问题。此外，相对ER的影响随智能体数量和算法选择而变化。
英文摘要：
In an infinitely repeated general-sum pricing game, independent reinforcement learners may exhibit collusive behavior without any communication, raising concerns about algorithmic collusion. To better understand the learning dynamics, we incorporate agents' relative performance (RP) among competitors using experience replay (ER) techniques. Experimental results indicate that RP considerations play a critical role in long-run outcomes. Agents that are averse to underperformance converge to the Bertrand-Nash equilibrium, while those more tolerant of underperformance tend to charge supra-competitive prices. This finding also helps mitigate the overfitting issue in independent Q-learning. Additionally, the impact of relative ER varies with the number of agents and the choice of algorithms.
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