What is Reinforcement Learning?
Reinforcement Learning uses a simple logic of learning in which the network tries to learn from the feedback it obtains. This tries to optimise the overall reward in the long run instead of the current reward.

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As stated by the wiki,
The basic reinforcement learning model consists of:


a set of environment states S;
a set of actions A;
rules of transitioning between states;
rules that determine the scalar immediate reward of a transition; and
rules that describe what the agent observes.


The rules are often stochastic. The observation typically involves the scalar immediate reward associated with the last transition. In many works, the agent is also assumed to observe the current environmental state, in which case we talk about full observability, whereas in the opposing case we talk about partial observability. Sometimes the set of actions available to the agent is restricted (e.g., you cannot spend more money than what you possess).