What Is Reinforcement Learning In Machine Learning?

A simple guide to Reinforcement Learning

Reinforcement Learning comes under Machine Learning. Machine Learning has the following kinds:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Reinforcement Learning is a type of Machine Learning where an agent learns how to behave in an environment by performing certain actions and learning from the results of those actions. When the agent takes action, it gets the reward on the basis of the result. This way the learning process continues depending on the positive and the negative reward. The learning is based on interaction with the environment. The agent discovers which action will give the maximum reward. Depending on that, the agent takes action. The following quote is true for Reinforcement Learning:

When it is not in our power to determine what is true, we ought to act in accordance with what is most probable. - Descartes

The following are the important components of Reinforcement Learning:

  • Agent - The one who takes the action.
  • Environment - Where the agent takes the action.
  • State - The situation in which the agent is.
  • Action - The thing which is done by the agent.
  • Reward - The measurement of success or failure of the action taken by the agent.

Reinforcement Learning loops around the state, action, and reward. The agent takes the action depending upon the state and gets the reward on the basis of that. There are different approaches to Reinforcement Learning:

  • Value-based
  • Policy-based
  • Model-based

There are two trade-offs in Reinforcement Learning as follows:

  • Exploration - Finding more information about the environment.
  • Exploitation - exploiting to maximize the reward.

This is Reinforcement Learning. That's it for now.

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