The Professor will present the paper: Reinforcement Learning in Equilibrium

Abstract: Reinforcement learning (RL) algorithms can be used to efficiently solve complex discrete time economic systems that are computationally too expensive for standard numerical methods. I introduce a Walrasian auctioneer into the popular Actor-Critic family of RL algorithms to allow for market clearing, and apply this new methodology to solve a dynamic equilibrium model of asset pricing under asymmetric information. The model features many assets with an arbitrary covariance structure, multiple strategic investors with heterogeneous private signals, uninformed non-strategic investors, and transaction costs. Unlike in standard strategic trading models, informed trading intensity in my model is reduced when the fraction of informed traders in the market rises, while return volatility is increased. The model generates complex trading dynamics, where investors with more precise private signals learn to front-run investors with less precise signals, leading to price overreactions and corrections despite all agents having rational expectations.