  Optimal sensor placement enhances the efficiency of a variety of applications
for monitoring dynamical systems. It has been established that deterministic
solutions to the sensor placement problem are insufficient due to the many
uncertainties in system input and parameters that affect system response sensor
measurements. Accounting for the uncertainties in this typically expensive
optimization is challenging due to computational intractability. This study
proposes a stochastic environment in the form of a Markov Decision Process and
a sensor placement agent that aims to maximize the information gain from
placing a particular number of sensors of different types within the system.
The agent is trained to maximize its reward based on an information-theoretic
reward function. To verify the efficacy, the approach is applied to place a set
of heterogeneous sensors in a shear building model. This methodology can be
used to accommodate uncertainties in the sensor placement problem in real-world
systems.
