  This exercise proposes a learning mechanism to model economic agent's
decision-making process using an actor-critic structure in the literature of
artificial intelligence. It is motivated by the psychology literature of
learning through reinforcing good or bad decisions. In a model of an
environment, to learn to make decisions, this AI agent needs to interact with
its environment and make explorative actions. Each action in a given state
brings a reward signal to the agent. These interactive experience is saved in
the agent's memory, which is then used to update its subjective belief of the
world. The agent's decision-making strategy is formed and adjusted based on
this evolving subjective belief. This agent does not only take an action that
it knows would bring a high reward, it also explores other possibilities. This
is the process of taking explorative actions, and it ensures that the agent
notices changes in its environment and adapt its subjective belief and
decisions accordingly. Through a model of stochastic optimal growth, I
illustrate that the economic agent under this proposed learning structure is
adaptive to changes in an underlying stochastic process of the economy. AI
agents can differ in their levels of exploration, which leads to different
experience in the same environment. This reflects on to their different
learning behaviours and welfare obtained. The chosen economic structure
possesses the fundamental decision making problems of macroeconomic models,
i.e., how to make consumption-saving decisions in a lifetime, and it can be
generalised to other decision-making processes and economic models.
