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This exercise proposes a learning mechanism to model economic agent'sdecision-making process using an actor-critic structure in the literature ofartificial intelligence. It is motivated by the psychology literature oflearning through reinforcing good or bad decisions. In a model of anenvironment, to learn to make decisions, this AI agent needs to interact withits environment and make explorative actions. Each action in a given statebrings a reward signal to the agent. These interactive experience is saved inthe agent's memory, which is then used to update its subjective belief of theworld. The agent's decision-making strategy is formed and adjusted based onthis evolving subjective belief. This agent does not only take an action thatit knows would bring a high reward, it also explores other possibilities. Thisis the process of taking explorative actions, and it ensures that the agentnotices changes in its environment and adapt its subjective belief anddecisions accordingly. Through a model of stochastic optimal growth, Iillustrate that the economic agent under this proposed learning structure isadaptive to changes in an underlying stochastic process of the economy. AIagents can differ in their levels of exploration, which leads to differentexperience in the same environment. This reflects on to their differentlearning behaviours and welfare obtained. The chosen economic structurepossesses the fundamental decision making problems of macroeconomic models,i.e., how to make consumption-saving decisions in a lifetime, and it can begeneralised to other decision-making processes and economic models.

Learning from zero: how to make consumption-saving decisions in a stochastic environment with an AI algorithm