Reinforcement learning
Reinforcement learning is a branch of machine learning in which a computer program learns to make decisions in an environment to achieve a specific goal. The goal is to enable the program to choose the best action based on the situation it finds itself in, receiving feedback on the quality of its choice.
How exactly does Reinforcement learning work?
This feedback is given in the form of rewards or punishments, depending on the program's performance. The program then adapts by learning which actions produce the best results in different situations. This process of learning and adaptation is repeated iteratively until the program has learned the optimal behavior to achieve its goal. Through trial and error, the program learns which actions produce the best rewards in a specific environment. This knowledge can then be applied in new situations to improve the program's performance.
