  The concept of an embodied intelligent agent is a key concept in modern
artificial intelligence and robotics. Physically, an agent is an open system
embedded in an environment that it interacts with through sensors and
actuators. It contains a learning algorithm that correlates the sensor and
actuator results by learning features about its environment. In this article we
present a simple optical agent that uses light to probe and learn components of
its environment. In our scenario, the quantum agent outperforms a classical
agent: The quantum agent probes the world using single photon pulses, where its
classical counterpart uses a weak coherent state with an average photon number
equal to one. We analyze the thermodynamic behavior of both agents, showing
that improving the agent's estimate of the world corresponds to an increase in
average work done on the sensor by the actuator pulse. Thus, our model provides
a useful toy model for studying the interface between machine learning, optics,
and statistical thermodynamics.
