  In this effort we consider a reinforcement learning (RL) technique for
solving personalization tasks with complex reward signals. In particular, our
approach is based on state space clustering with the use of a simplistic
$k$-means algorithm as well as conventional choices of the network
architectures and optimization algorithms. Numerical examples demonstrate the
efficiency of different RL procedures and are used to illustrate that this
technique accelerates the agent's ability to learn and does not restrict the
agent's performance.
