What is Reinforcement Learning? A Detailed Explanation

There are so many new things that we hear from time to time and one of the most important things that we are using now is machine learning. There is so much learning included as part of machine learning but today we are going to throw some light on reinforcement learning. As there are various segments associated with this, we are going to lean more towards this and expand our knowledge. If you are someone who is looking forward to having a career in machine learning then this page is going to be very useful for you. You can learn all about big data and machine learning with the best page available online and then have a better chance at a career. Here in this article, you are going to learn all about what is reinforcement learning, its benefits, its types and real-life applications. Later we are going to compare it with supervised learning so that you have a great understanding and have a good start to your career.

What is Reinforcement Learning?

If a program needs to make the sequence of decisions using machine learning, then the best kind of learning to go for is reinforcement learning. In this type of learning, the machines are trained and they make those decisions that are in a sequence. If we talk about the broader picture, it is basically the decision making science. Here the main focus is to have the maximum reward which is achieved by the best behavior in the given environment. There are various interactions by which the model learns and then when the optimal behavior is found, it is adapted with the maximum accurate results.

Reinforcement learning is very different from supervised learning as in supervised learning, the correct answer key is present and the model works in that direction and finds the best algorithm to find that. But here in reinforcement learning, there are no right answers and the system is exposed to various possibilities. There is no data set that is used for the training in this kind of learning, so the model is going to learn itself from the experience in that system. As the model is going to be developed by various hits and trials, there is going to be the maximum benefit and the most accurate results can be developed, therefore, it is becoming one of the main choices for data scientists all over the world.

How does Reinforcement Learning work?

The main idea in reinforcement learning is that a goal needs to be achieved and this involves an agent who is responsible for exploring the unknown environment and finding that particular goal. There is a presence of the cumulative reward and all the goals that are present are described here in this environment. The state of the given environment is determined by the given agent and the best way in which the maximum reward is obtained is the most perfect solution. The right data science online course is going to be very helpful for you to understand all these concepts in depth and know more about them. There are so elements in the RL system and those are defined below:

The agent which can also be a learner in the whole system
The given environment in which the agent will be interacting with
There is a policy present that is followed by the agent to explore the environment.
Then there is the signal which points towards the reward that the agent is going to have for working on the desired actions.

The reward is converted into an algorithm that helps the system to understand how to interact with the systems in the future and get the best outcome. There are various kinds of algorithms that are being used in reinforcement learning. Some of them are mentioned below:

Markov Decision Process: Particularly known as MDPs, this is the kind of framework that is used to model the overall process for decision making. The above-mentioned attributes like the value, the state, and rewards are the main part of this framework. They are considered as one of the most popular algorithms while resolving problems using RL.
SARSA: SARSA expands to State Action Reward State Action. It is basically an algorithm that is used to learn the MDP policy. There is an agent which will select and perform the action in the current given state and get a reward for that. Then the new state is going to be assigned which will have a new action.
Q-Learning: It is one of the self-directing models in reinforcement learning and there is no policy, no value or actions.
Deep Reinforcement Learning: If you are looking for algorithms that can work with large data sets then going with deep reinforcement learning can be very helpful. There are various complex and high dimensional problems, and those can be resolved by this.

What are the Advantages of Reinforcement Learning?

After learning how this kind of learning works, it is time for us to look at why reinforcement learning is becoming the first choice for many data scientists!

If we talk about the other kinds of learning, the data sets that are available need to be labeled and this is going to cost too much. The amount of data sets is growing day by day and it becomes very difficult to label them. The good thing about reinforcement learning is that here you do not need the labeled data sets and you can use them, so this is going to save a lot of time and cost for the organizations.
Another great advantage of having the RL is that the system is going to find a new approach to work for a given problem. In another kind of learning, sure the system is going to learn to do the job in a more efficient way than before, but it is never going to find a new solution to the problem. That can be made possible when the RL module is being introduced in the system.
You are going to have bias-free results when you are going to use the RL in the models. If there is a bias in the training data sets, then the machine is going to pick up and give inaccurate results. But with the RL module, the system is better equipped to find better results with bias resistance.
Also, when working with the RL training module, the machine is always going to work for the sequence of the action rather than just focusing on the input-output kind of actions. Here the new dimensions to the given data sets can be discovered, unlike other training modules.
With the RL, the team is going to have an adaptable approach to finding the solution. There is retraining required when you are using other learnings like supervised and a lot of time is invested again. But in reinforcement learning, the machine is going to adapt to the new environment and no retraining is going to be required.

These are the five advantages that top the chart for reinforcement learning and the reasons why more and more organizations all over the world are adapting this approach.

Examples for Reinforcement Learning

Coming to the reinforcement training examples, there are so many real-life examples where this module is being used. As sequential training is provided and the system is exposed to the new environments, they are able to learn from their experiences. So the common examples are:

Computer Games

One of the major examples where reinforcement learning is being used is computer games. There are various games like PAC Man where this learning is used and the experience of the game is enhanced.

Automation and Robotics

There are various tasks that are needed in robotics and industrial applications. Using this kind of learning, the machine can learn to develop the skill and complete the task in a better way.

Driving and other application

The auto-driving system is also based on reinforcement learning where the system sees and adapts to the new environment all the time and takes action as per the given situation.

Advertising

Using reinforcement learning, various companies will be able to market their product in a better way. This learning can be used to create personalized campaigns and advertisements for them that can be used for better marketing strategies.

What are the Different Challenges of Reinforcement Learning?

Extensive experience: This is one of the major challenges that data scientists have to go through. There are data sets that are needed for the agent to pick the right action and have a reward for that. Also, when there are complex environments present that are high dimensional as well, this becomes even more extensive and more exploration is needed to find the best solution.
Need for large data sets: The agent needs to explore much more before the maximum reward is being captured. So to work better with the RL there should be a large data set.
Delayed rewards: When there are many scenarios in the data sets, and the agent has to run through all the possible scenarios to get the optimal outcome, there is a delay in the reward. The optimal policy gets delayed as there are large data sets to be discovered before the decision is to be made.
Design of reward structure: Another great challenge is the designing of the reward structure for the RL based training. In the end, the actions and the sequence of work to be done by the agent depends on the reward structure and how it is trained to perform a task.

Difference Between Reinforcement Learning and Supervised Learning:

Now that we have discovered reinforcement learning, it is time for us to compare it with other training modules and see which one is better for a given situation.

In supervised learning, the supervisor is required to be responsible for the labeling of data sets. These data sets are being fed to the training algorithms and they have a model developed based on this. In this, the extrapolation is being done and predictions are being made from the given large set of data.

But if we talk about the Reinforcement Learning based training, this is a different kind of module. In this, no supervisor is required and the data sets are not labeled as well. Here in this, the agent is exposed to the environment, where it interacts with the different given circumstances and then makes the observation. There is no defined data set in this. This factor is one of the major differences between supervised and reinforcement training.

In supervised learning, the decision is mostly based on the initial input or the input that is provided in the beginning. But in reinforcement learning, this is mostly done in a sequential manner. The output of a given action will depend on the initial input and then the next input will depend on the output of the previously given input.

There are various reinforcement training examples that can be used to differentiate between these two kinds of learning. The chess game is the best example for reinforcement learning as the next move is going to be the action based on the previous move. The game is going to learn from the environment and have the best reward which can be the winner in the match. For supervised learning, object recognition can be the best example. Here, the set of inputs and defined sets of output can be helpful in recognizing the object.

Future of Reinforcement Learning

There is a lot of scope in deep reinforcement learning. In recent times, many new studies are going on for this and with the Data Science Training major milestones are being achieved. There are so many real life-based applications where reinforcement learning is being implemented and there are many types of research going on the same hand. As there are various advantages of this learning, new methods are getting discovered every day.

Final Word

The demand for machine learning is increasing exponentially and there are so many advantages that are yet to be discovered. So, if you are also interested in this career and want to try your hands on this training and application, then you can learn from the best Data Science Certification Course from StarAgile. Here not only, you will be given the best material to learn from, but you are also going to have real examples to work on. So do not think more about and start with the progress in your career.

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