In an infinitely repeated pricing game, pricing algorithms based on artificial intelligence (Q-learning) may consistently learn to charge supra-competitive prices even without communication. Although concerns on algorithmic collusion have arisen, little is known on underlying factors. In this work, we experimentally analyze the dynamics of algorithms with three variants of experience replay. Algorithmic collusion still has roots in human preferences. Randomizing experience yields prices close to the static Bertrand equilibrium and higher prices are easily restored by favoring the latest experience. Moreover, relative performance concerns also stabilize the collusion. Finally, we investigate the scenarios with heterogeneous agents and test robustness on various factors.

中文翻译：

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通过经验重放了解算法合谋

在无限重复的定价游戏中，即使没有沟通，基于人工智能（Q学习）的定价算法也可能会持续学习收取超竞争价格。尽管引起了对算法合谋的关注，但对潜在因素知之甚少。在这项工作中，我们通过经验重播的三种变体对实验的动力学进行了实验分析。算法合谋仍然根植于人们的喜好。随机化经验可以使价格接近静态Bertrand均衡，并且可以通过青睐最新经验轻松恢复较高的价格。而且，相对性能方面的担忧也稳定了合谋。最后，我们调查了具有异构代理的场景，并测试了各种因素的鲁棒性。