We present a novel unified bilevel optimization-based framework, PARL, formulated to
address the recently highlighted critical issue of policy alignment in reinforcement learning …

The application of Deep Reinforcement Learning (DRL) in economics has been an area of
active research in recent years. A number of recent works have shown how deep …

Understanding and promoting subjective wellbeing (SWB) has been the topic of increasing
research, due in part to its potential contributions to health and productivity. To date, the …

In reinforcement learning (RL), a reward function is often assumed at the outset of a policy
optimization procedure. Learning in such a fixed reward paradigm in RL can neglect …

We introduce the use of generative adversarial learning to compute equilibria in general
game-theoretic settings, specifically the generalized Nash equilibrium (GNE) in pseudo …

In a Markov decision process (MDP), an agent interacts with the environment via
perceptions and actions. During this process, the agent aims to maximize its own gain …

Real-world economies can be modeled as a network with many heterogeneous and
strategic agents. In this setting, it is very challenging to find optimal mechanisms, eg, taxes …

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic
simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model …

We introduce a multi-agent simulator for economic systems comprised of heterogeneous
Households, heterogeneous Firms, Central Bank and Government agents, that could be …

In various applications, the optimal policy in a strategic decision-making problem depends
both on the environmental configuration and exogenous events. For these settings, we …