What is reinforcement learning?
Reinforcement learning is an area of machine learning. It is about taking appropriate action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. In reinforcement learning, as there is no training dataset, so there is no answer, but the reinforcement agent decides what to do to perform the given task. Without a training dataset, it is bound to learn from its experience.
What are the types of reinforcement?
There are two types of reinforcement:
- Positive Reinforcement: When an event occurs due to a particular behavior and increases the behavior’s strength and frequency. In other words, it has a positive effect on behavior.
- Negative Reinforcement: It is defined as strengthening the behavior because a negative condition is stopped or avoided.
What are the applications of reinforcement learning?
- Robotics for industrial automation
- Machine learning and data processing
- Creation of training systems that provide custom instruction and materials according to the requirements of users