噪声对收敛博弈学习动力学的影响
Effects of noise on convergent game learning dynamics
摘要：
我们研究了滞后锚动态的随机效应，这是一种用于在迭代游戏中学习成功策略的强化学习算法，在没有噪声的情况下收敛到纳什点。当参与者对对手的策略倾向只有有限的信息时，动态是随机的。这种噪声的影响进行了研究分析的情况下，它是小的，但有限的，我们表明，波动的统计和相关性可以计算到一个高精度。我们发现，该系统可以表现出准周期，由固有噪声驱动。如果参与者是不对称的，并且使用不同的参数进行学习，那么由于这些围绕确定性均衡的随机振荡，可以实现净收益优势。
英文摘要：
We study stochastic effects on the lagging anchor dynamics, a reinforcement learning algorithm used to learn successful strategies in iterated games, which is known to converge to Nash points in the absence of noise. The dynamics is stochastic when players only have limited information about their opponents' strategic propensities. The effects of this noise are studied analytically in the case where it is small but finite, and we show that the statistics and correlation properties of fluctuations can be computed to a high accuracy. We find that the system can exhibit quasicycles, driven by intrinsic noise. If players are asymmetric and use different parameters for their learning, a net payoff advantage can be achieved due to these stochastic oscillations around the deterministic equilibrium.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
