The hidden linear algebra of reinforcement learning

How do fundamentals of linear algebra support the pinnacles of deep reinforcement learning? The answer is in the iterative updates when solving Markov Decision Process.

Reinforcement learning (RL) is the set of intelligent methods for iteratively learning a set of tasks. As computer science is a computational field, this learning takes place on vectors of states, actions, etc. and on matrices of dynamics or transitions. The states and vectors can take different forms, but how can we look at the convergence of the algorithms making headlines…