This working paper explores how integrating artificial intelligence (AI) into a general equilibrium framework can enhance theoretical modeling by representing adaptive behaviors that integrate perspectives from neuroscience and psychology. Using a deep reinforcement learning (RL) approach, the model highlights how agents explore options, balance objectives, and adjust strategies during an economic regime change, such as an acceleration in the money supply process. Simulation results illustrate that AI agents, guided by exploration-driven learning, adapt their consumption, savings, and liquidity holding decisions in response to structural changes.