An Introduction to Reinforcement Learning

To train the machines and make them capable of making certain decisions the field of reinforcement learning is required. It is the sub-domain of Machine Learning that helps in the automation of machines and making them intelligent. It requires the study and implementation of various algorithms that would understand the patterns and paths followed at certain decisions. It involves the deep insight of two separate components – supervised learning and unsupervised learning.

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In this technological era, where all the innovative brains are trying to train and deploy models such that they could make the best decisions using artificial intelligence, the field of reinforcement learning provides one of the best pathways for the same. To train the machines so that they can make intelligent decisions, various technologies are required like machine learning, deep learning, artificial intelligence, reinforcement learning, big data analysis, and much more.

Reinforcement learning is the branch of Machine Learning. It trains the machines so that they can choose the best possible pathway among various paths. This is based on unsupervised learning. It requires various software and high-quality training data sets to train the model. 

In reinforcement learning, the training dataset is provided to the model. It doesn’t know the correct output at the beginning, i.e., the correct output isn’t mentioned in the training dataset. In supervised learning, the training dataset contains the input as well as the desired output. In this way, reinforcement learning differs from supervised learning. In reinforcement learning, the model has to decide the best path for a problem itself. In this way, the model trains on the training dataset provided. 

But if the training dataset is not provided then the machine learns from the previous experience that it has. The reinforcement agents evaluate and decide the proper path and the suitable decision possible for a particular input and condition. 

This learning is like trial and error. The environment provided for evaluation is uncertain. The machine needs to choose a sequence of paths based on trial and error. It is just like a reward game, in which, for each correct path or procedure the machine or bot is being awarded, and for each wrong input or path or procedure penalties are charged. This is done to train the machine in a random and uncertain environment. This is similar to unsupervised learning. 

In this way, the machines are made creative. Here from a random, uncertain, and clueless environment, we train the machine to give high-quality output so that they can make intelligent decisions. This boosts the performance of the model as they learn on a trial and error basis without any information of desired output in the training dataset. The model has no previous hints on how to solve a particular problem. It only has the training dataset input if the training dataset is provided or else it is dependent on the previous learning. 

It has its explicit goals associated with each type of reinforcement learning. We will discuss those also. 

Important Things to Note on Reinforcement Learning

  • Reinforcement learning is a branch of machine learning. There exists a kind of relationship between supervised learning and reinforcement learning in the case of inputs and outputs but reinforcement learning differs from supervised learning as it doesn’t have desired output in the training dataset. The output is generated based on trial and error and the machine learns on its own. Hence, it relates to unsupervised learning.
  • One needs to know how to formulate a basic reinforcement learning problem. For this, one needs to have information about the environment and the current situation of the reinforcement agent or the machine. Then mapping of the actions, or the states of the agent or machine is done. For the correct state, the agent is awarded and for the incorrect state, penalties are there.
  • There are various reinforcement learning algorithms. The widely used reinforcement learning algorithms are Q-Learning and SARSA (State-Action-Reward-State-Action). In Q-Learning, the model learns from the previous actions or state of the agent whereas in SARSA. The agent learns from its current state.
  • There are various applications of reinforcement learning. It includes the formation and advancement of video games. It is the key factor in automation and development. It is required in text summarization engines, for creating an efficient adaptive control system. Stock trading, robot manipulation, and much more.

Major Types of Reinforcement Learning

There are four types of reinforcement learning. It includes positive, negative, punishment, and extinction. These are broadly described below. 

Positive Reinforcement

This includes the addition of a value to increase the chances of getting the desired outcome. As in reinforcement learning, the model learns on its own based on trial and error. So, it includes the addition of such values due to which the model will most probably choose that pathway that will lead it towards giving the desired output. It is the addition or incrementation of certain values so that we get the most suitable output.

This can be understood by certain examples. Suppose, we add the condition that the students participating in the second event along with the first event would get 75% off on the fee of both events then there is a high probability that people will participate in both the events.

Negative Reinforcement

This is the opposite of positive reinforcement but has the same goal, i.e., to increase the chances of making the right decision. It includes the removal of certain values which could increase the probability of the making right decisions by the machine. It will lead the machine to select those pathways that would provide the desired output. In this way, by removal of unnecessary or additional values that could mislead the algorithm, we define a proper pathway for proceeding towards a successful outcome.

This can be understood by certain examples. Suppose, we take away a mobile phone from a child. So, the chances of his eyesight weakness will decrease.

Punishment (Positive Punishment)

It is done to decrease certain behavior values or to decrease the chances of taking certain paths by the reinforcement agent or the machine. This is done by adding some unlikely or disinclination value of a specific path or behavior so that the chances of selecting that option become less and we get the desired output. This has been a successful form of reinforcement learning. 

Extinction (Negative Punishment)

It is just the opposite of positive punishment reinforcement but with the same goal. It includes the removal of certain values or behavior of specific pathways to reduce their chances of getting selected. It is the most powerful and effective form of reinforcement learning. The addition of unlikely value decreases the chances of selection of unwanted path but the removal of certain high required values decrease the probability of selection of that unwanted path at a higher rate.

In this way, it provides the best way to get the required output.

Reinforcement Schedules

As we learned about the four types of reinforcement learning, we might require to apply those repeatedly, in a proper sequence. This is known as Continuous Scheduling. 

The variable schedules are the most effective and efficient ones as compared to fixed schedules. This should maintain the consistency of the overall flow of the algorithm.

Applications of Reinforcement Learning

There are various applications of reinforcement learning, majorly related to automation and robotics. It has its roots in text manipulation, data mining, video games, and much more. We will see some broad applications and categories requiring reinforcement learning.

  • It is used in the manufacturing and advancement of robots. Along with deep learning, it is used in the advancement of various skills and technologies used in the manufacturing of robots to make them intelligent. These robots are then used in various fields and domains where research or labor work is required based on the decision that they need to make.
  • It is required in the stocking. In inventory management, it has its deep roots. It helps in providing optimized solutions by enabling proper space utilization and efficient operations. Similarly, the Q-Learning algorithm is used to provide a solution to the Split Delivery Vehicle Routing problem. Hence, it is used in delivery management also.
  • It is used in the security of various power systems. The Q-Learning algorithm is very beneficial in this case. It helps in maintaining the transmission power loss, multiple fuel options, valve point loading effects, and much more. 
  • It is used in computer clusters. It provides the solution where the algorithms are designed in such a way that they allocate the few or limited resources to various tasks in such a way that are utilized efficiently. Hence, it has its application in traffic light control systems, web system configuration, robotics, and much more.
  • Apart from this all, it is required in providing recommendations based on your search history. It bidding and advertising advice. Similarly, it is used in games.

There are numerous such applications of reinforcement learning as its main aim is towards automation and making the machines intelligent. As each industry requires data as fuel, without the proper procedure of processing it as they don’t have the desired output type in the training dataset, the need for reinforcement learning is increasing.

The scope of reinforcement learning is widely increasing and this technology would reach greater heights in the technical industry.

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