  The goal of this manuscript is to conduct a controltheoretic analysis of
Temporal Difference (TD) learning algorithms. TD-learning serves as a
cornerstone in the realm of reinforcement learning, offering a methodology for
approximating the value function associated with a given policy in a Markov
Decision Process. Despite several existing works that have contributed to the
theoretical understanding of TD-learning, it is only in recent years that
researchers have been able to establish concrete guarantees on its statistical
efficiency. In this paper, we introduce a finite-time, control-theoretic
framework for analyzing TD-learning, leveraging established concepts from the
field of linear systems control. Consequently, this paper provides additional
insights into the mechanics of TD learning and the broader landscape of
reinforcement learning, all while employing straightforward analytical tools
derived from control theory.
