This paper formulates a gradient-based reinforcement learning (GRL) model within a game-theoretic machine learning framework where players start from their initial circumstances with dispersed information, using the expected gradient to update choice propensities, and converge to the predicted...

This paper conducts marginal analysis and refi nes the updating rules for the adaptive learning models (e.g., Erev & Roth 1998) based on approaches in computer science. We propose Policy Gradient Reinforcement Learning (PGRL) to simulate the equilibration process of a decentralized market...

This study conducts a reinforcement learning (RL) experiment to investigate the behavior of noise traders under adverse selection. The experiment applies the concepts of Lo’s (2004) adaptive market hypothesis to limit order markets and offers a rational, adaptive learning-based explanation for...