Reinforcement learning is a machine learning technique in which a machine automatically learns to interact with an environment by performing the actions and seeing the results of actions in the surrounding. We do not need to direct or program the machine to act because it learns from its own experience and explores the environment by itself.
It learns positive and negative actions both because it continuously tracking the surrounding action and according to that it acts in the environment. The machine behavior depends upon the action performs in the environment, if the negative action performed in the environment then the machine will act with negative actions and vice versa.
Types of reinforcement learning:
It means adding positive action to increase the tendency of machines to act in some direction and it has a positive effect on the behavior. It also increases the strength of the behavior which maximizes the performance of the machine and this is making the behavior more likely to happen again in the future.
It is different from positive reinforcement and it is a process that can be used to increases the tendency that the specific behavior and the behavior result in taking something unpleasant away. It can be more effective than positive reinforcement depending on the situation and behavior.
Uses of Reinforcement Learning
In the real world, Reinforcement learning helps us to achieve high productivity at a very low cost and less time. It also helps us to automation to reduce human intraction. It provides intelligency to any machine to learn or explore the environment by itself or by its own experience.