Reinforcement learning provides a framework for solving dynamic optimization problems. This article offers a direct example of applying Q-learning to train an agent that conducts monetary policy in a simplified economic environment. This project is broken down into three core coding sections. I begin by defining the EconomyEnv class, which simulates the simplified economy and contains its fundamental rules, including the state variables (inflation, interest rate) and the agent’s action space. Next, I build the QLearningAgent class, whose responsibility is to learn the optimal policy by updating its Q-table based on rewards. The final section brings these two together, managing the training process, hyperparameter settings, and the discretization of the continuous state space required for a tabular Q-learning approach. ...