Reinforcement Learning

Reinforcement learning is an area of Machine Learning. Reinforcement. It is about taking suitable 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. Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of training dataset, it is bound to learn from its experience.

Main points in Reinforcement learning 

Input: The input should be an initial state from which the model will start

Output: There are many possible output as there are variety of solution to a particular problem

Training: The training is based upon the input, The model will return a state and the user will decide to reward or punish the model based on its output.

  • The model keeps continues to learn.
  • The best solution is decided based on the maximum reward.
  • Reinforcement learning is all about making decisions sequentially. In simple words we can say that the out depends on the state of the current input and the next input depends on the output of the previous input
  • In Reinforcement learning decision is dependent, So we give labels to sequences of dependent decisions

Example: Chess game

Types of Reinforcement: There are two types of Reinforcement:


Positive Reinforcement is defined as when an event, occurs due to a particular behavior, increases the strength and the frequency of the behavior. In other words it has a positive effect on the behavior.

Advantages of reinforcement learning are:

  • Maximizes Performance
  • Sustain Change for a long period of time

Disadvantages of reinforcement learning:

  • Too much Reinforcement can lead to overload of states which can diminish the results
  • Negative 
  • Negative Reinforcement is defined as strengthening of a behavior because a negative condition is stopped or avoided.

Advantages of reinforcement learning:

  • Increases Behavior
  • Provide defiance to minimum standard of performance
  • Disadvantages of reinforcement learning:
  • It Only provides enough to meet up the minimum behavior

Various Practical applications of Reinforcement Learning :

  • RL can be used in robotics for industrial automation.
  • RL can be used in machine learning and data processing
  • RL can be used to create training systems that provide custom instruction and materials according to the requirement of students.