minimize such error in order to get a good approximation of the original data.

In matrix completion, the general goal is to recover a complete matrix from a few given observations. It is usually impossible to complete an arbitrary matrix with only partial observations, so additional assumptions are needed to proceed. A crucial assumption for example is that the matrix has rank much smaller than the number of its rows and columns. In this context, the Frobenius norm is the most commonly used norm to minimize the error incurred by the matrix completion method.

Reinforcement Learning
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In reinforcement learning, the goal is to learn via interaction and feedback, or in other words learning to solve a task by trial-and-error. The mathematical foundation for reinforcement learning is based on Markov Decision Processes (MDPs), which we define below.

A discrete-time MDP is a tuple $(S,A,T,r,\delta)$ where $S$ is a finite set of states, $A$ is a finite set of actions, $T$ is the set of transition probabilities, $r$ is a reward function, and $\delta \in (0,1)$ is a discount factor. Loosely speaking, reinforcement learning aims at maximizing the reward function for a state $s \in S$ and an action $a \in 