Description
Organizer and chair: Janusz Meylahn
I characterize the outcomes of a class of model-free reinforcement learning algorithms, such as stateless Q-learning, in a prisoner's dilemma. The behavior is studied in the limit as players stop experimenting after sufficiently exploring their options. A closed form relationship between the learning rate and game payoffs reveals whether the players will learn to cooperate or defect. The...
This article challenges the idea of algorithmic collusion as proposed in Calvano et al. (2020) and subsequent literature. Identifying a critical mistake, we dispute the notion that supracompetitive prices result from collusive mechanisms where high prices are sustained by reward and punishment strategies. Instead, our analysis suggests that both phenomena originate from simultaneous...
Recent advances in decentralized multiagent reinforcement learning (MARL) have led to the development of algorithms that are provably convergent in a variety of Markov game subclasses. One of these is the Decentralized Q-learning (DQ) algorithm by Arslan and Yüksel (2017) which is provably convergent in weakly acyclic games. In this talk, I will present a new characterization of weak...
