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arXiv Machine Learning By Georgios C. Chasparis

Aspiration-based Perturbed Learning Automata in Games with Noisy Utility Measurements. Part A: Stochastic Stability in Non-zero-Sum Games

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The paper introduces aspiration-based perturbed learning automata (APLA), a payoff‑based learning scheme that incorporates an aspiration factor to reinforce action selection in distributed multi‑player games. It presents a stochastic stability analysis of APLA in positive‑utility games with noisy observations, establishing that the infinite‑dimensional Markov chain induced by the dynamics can be reduced to a finite‑dimensional one. This work extends previous results beyond potential and coordination games to generic non‑zero‑sum games, with a second part focusing on weakly acyclic games.

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