Explain Reinforcement learning

  • Reinforcement learning is a type of machine learning where an agent interacts with the environment and learns by his actions and outcomes. On each good action, he gets a positive reward, and for each bad action, he gets a negative reward. Consider the below image:

Data Science Interview Questions

  • The goal of an agent in reinforcement learning is to maximize positive rewards.
  • In reinforcement learning, algorithms are not explicitly programmed for tasks but learns with experiences without any human intervention.
  • The reinforcement learning algorithms is different from supervised learning algorithms as there is no any training dataset is provided to the algorithm. Hence the algorithm automatically learns from experiences.