# Overview of Distributed Machine Learning Techniques for 6G Networks
## 3 Machine Learning in Wireless Communication
### Reinforcement Learning  
Reinforcement learning (RL) seeks the correlations between situations and actions in order to maximize a numerical result [20; 21]. The three characteristics mentioned below are the three most significant characteristics of RL implementations. It is essentially a kind of closed loop, as the actions of the learning system influence its future inputs. Furthermore, unlike many ML domains, the learning system is not taught about the action to be taken, but is instead asked to figure out which action is the most convenient for it to execute. In the most interesting circumstances, the actions have an impact on not only the immediate but also all subsequent consequences. The trade-off between exploration and exploitation (see [20]) is a key difficulty in RL that does not exist in most other types of learning frameworks. To optimize earnings, a learning agent must prioritize behaviors that have been tested and proven to be productive in the past. To uncover and recognize these activities, though, it will have to try with ones that have never been done before. A learning agent must therefore exploit what it knows in order to gain rewards, while also exploring in order to find any margins for optimization. Neither option can be carried out effectively on its own. In addition to the agent and the environment in which it is located, there are four fundamental elements that make up a RL system: a _policy_, a _reward signal_, a _value function_, and optionally, a _model of the surrounding environment_. A policy specifies how an agent should act at a specific point in time. A policy, in general, is the mapping of perceptions of environmental conditions into actions to be carried out when you are in them. It corresponds to what is known as the collection of rules or stimulus-response associations in psychology. Within RL, the goal is defined by a reward signal. The environment provides the agent a number and a reward, at regular intervals. In the long run, the agent's ultimate goal is to maximize total rewards. As a result, the reward signal tells the agent which events are positive and which are negative. They are the immediate characteristics that define the problem faced by the agent. The reward received by the agent always depends on the current action and state of the environment. Given that, the reward signal indicates the goodness of an action in the immediate future; the value function specifies which is the best choice in the long run. Using the appropriate simplifications, the value of a state can be understood as the total amount of reward that an agent expects to accumulate in the future, starting from that state. The rewards determine the immediate intrinsic desirability of the state in which the agent operates, whereas the values determine the long-term desirability of the state in which the agent operates, taking into account the most likely sequence of states. Rewards are in a sense a primary magnitude, whereas values, as reward predictions, are a secondary magnitude. However, it is the values that should be taken into account most when evaluating a decision. The choice of an action is made based on values, as what you want is a choice that leads to a high value of rewards in the long run. The values are much more complicated to estimate, whereas the rewards are provided by the environment itself. It is no coincidence that the most important component of almost all RL algorithms is the value estimation method. The model of the environment is the fourth and last element. It is something that tries to mimic the behavior of the environment, or more accurately, something that helps to predict how the environment will behave. Models are used in programming as decision-making tools for a set of activities that take into account potential future events before they occur. The fact that RL uses training information to evaluate the actions taken, rather than instructing by offering the proper actions to take, is the most fundamental element that distinguishes it from other methods of learning.