Reinforcement Learning
Reinforcement learning (RL) is a machine learning technique that focuses on training an algorithm following the cut-and-try approach. The algorithm (agent) evaluates a current situation (state), takes an action, and receives feedback (reward) from the environment after each act. Positive feedback is a reward (in its usual meaning for us), and negative feedback is punishment for making a mistake.
RL algorithm learns how to act best through many attempts and failures. Trial-and-error learning is connected with the so-called long-term reward. This reward is the ultimate goal the agent learns while interacting with an environment through numerous trials and errors. The algorithm gets short-term rewards that together lead to the cumulative, long-term one.
So, the key goal of reinforcement learning used today is to define the best sequence of decisions that allow the agent to solve a problem while maximizing a long-term reward. And that set of coherent actions is learned through the interaction with environment and observation of rewards in every state.