What is Reinforcement Learning ?

Reinforcement learning is a machine learning method in which the machine learns by experimenting with positive and negative rewards. If an experiment is performed and the results are fruitful, then it is a positive reward; if the results are not as expected, then it is a negative reward.

Let's understand this with an example: When humans learn to ride a bicycle, they pedal the bicycle and it moves, which gives a positive experience, but when balance is not maintained, they fall, which is a negative experience. Hence,  learning from experiences is known as "reinforcement learning."

Terms associated with reinforcement learning

• Agent : Entity that explores the environment and then act on it.

• Environment : Defined as surroundings in which the agent is surrounded by, which is random in general

• Action : Moves that are taken by agent within the environment

• State : Situation which is returned by environment after agent takes the action

• Reward : Feedback received from environment after agent has taken the action

• Policy : Strategy applied by agent for the next action basis current state

• Value :  Expected long terms return of agent considering discount factor

• Q-Value : Similar to value but it also takes current action in account