Reinforcement Learning is used to teaches a machine or agent based on trial and error method by using input from environment, and reward and punishment based on feedback(I.e generated output’s quality). In reinforcement learning, a learning system called an agent can perceives the environment, performs some actions, and gets rewarded or penalized depending on how it is performing. The main goal of the agent is to accumulate as much as rewards as possible. In order to maximize reward. the agent learns the best strategy(policy) necessary.
ℹ️ Reinforcement Learning doesn’t have a training dataset and is utilized in solving interactive problems.
Types of algorithms: