Reinforcement learning is machine learning that enables a model to take the best possible path for a particular situation. This is done by maximizing the reward for action. Reinforcement learning differs from supervised learning in the way that in supervised learning, the training data has the answer key with it, so the model is trained with the correct answer itself. In contrast, there is no answer in reinforcement learning, but the reinforcement agent decides what option to choose to perform the given task. In the absence of a training dataset, it learns from its experience.
HOW DOES IT WORK?
In reinforcement learning, the algorithm is given a positive reward in the form of +1 for taking the right action and in the case of the wrong action, it is given a negative reward in the form of -1. Thus, ensuring the model will take a similar course of action in a similar situation to ensure maximum reward.
Various models such as Q-learning and Markov decision process are widely used for reinforcement learning.
Markov Decision Process
This model is used for situations where the outcome is partly in the decision maker’s control.
The model attempts to predict an outcome given only information provided by the current state. However, the Markov decision process incorporates the characteristics of actions and motivations during each step. At each step during the process, the decision maker may take action in the current state, resulting in the model moving to the next step and offering the decision maker a reward.
Q-Learning is a Reinforcement learning algorithm that will find the next best action, given a current state. It chooses this action at random and aims to maximize the reward.
In this, the reward is given for each step that leads to the final goal and no rewards are given for steps that do not lead to the final goal.
Reinforcement learning is widely used for the automation of robots so that they can handle unexpected events and perform well in them.
It is also used for applications requiring the best features to gain better results.
Some field of application of Reinforcement learning is-
· It is widely used for the automation of industrial robots
· It can be used for Business strategy planning
· It is used in machine learning and data processing
· It helps you to create training systems that provide custom instruction and materials according to the requirement of tasks.
· It is used for aircraft control and robot motion control
· Reinforcement learning can also be used in Trading as the model learns through experience to predict the stock prices, and thus trading is a suitable location for its application
Reinforcement learning models can allow robots to take different actions in scenarios leading to the best possible path and thus are widely valued in the field of automation. It has a high value in applications of path finding and can be used in rovers and autonomous navigation bots. Thus, reducing the human supervision for the movement of the bot.