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Reinforcement Learning in Machine Learning

Posted by narayana on January 3rd, 2020

What is Reinforcement Learning?

Reinforcement Learning(RL) is a type in Machine Learning. It allows a user to determine interactive conditions, by using trial and error methods with responses from its own activities and experiences.

Both Reinforcement and supervised learning uses mapping among output and input, but in supervised learning, the feedback is given to the user as the right set of activities, to do a piece of work. Whereas, in RL of Machine Learning, it uses profits and penalties which are indicators, for positive and negative actions.

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Example of Reinforcement Learning:

Let us see the example of RL in Machine Learning, in a computer game, there are a robot and a reward, that has many obstacles in the middle. The robot has to find the best way, to get the reward. 

In this case, the robot understands by trying different paths and then decides the path that gives him the rewards, with fewer obstacles. Every correct step will give the robot a reward whereas, every wrong step will minus the reward. The overall rewards will be estimated, after reaching the last reward.

What are the Types of Reinforcement Learning?

In Machine Learning, RL is of two types. They are

  1. Positive – If a situation because of specific behavior, improves the frequency and strength of the behavior. In simple words, if it has a positive result on the behavior.

Benefits:

  • Improves performance.

  • Supports change for a long time

Drawbacks:

  • Excessive Reinforcement may result in too many states, that can decrease the results.

  1. Negative – Increasing the behavior strength by avoiding or stopping a negative situation, is known as Negative Reinforcement.

Benefits:

Improves Behavior

Resistance to small performance standards.

Drawback:

It delivers only necessary to get the least possible behavior.

How does Reinforcement Learning work?

In Reinforcement Learning technique of Machine Learning, there are various solutions to an issue. It allows the software agent to choose an action, that will increase the benefits in the long run. Those kinds of algorithms have an infinite point of view.

Practically, this is performed by understanding how to determine the value of a specific state. These measures can be changed later, by using part of the next state’s benefits.

If all the actions and states are tried enough times, then it will help to define the best plan. Among all actions, the action that increases the next state value is chosen.

Learn for more Artificial intelligence vs Machine learning

Challenges in Reinforcement Learning:

In the RL Technique of Machine Learning, we face many challenges. One of the challenges is it is very memory expensive for saving values of every state, as the issues can be very difficult.

To solve these issues, we have to use value approximation methods like Decision Trees or Neural Networks. There are many effects of using these imperfect value determinations, and RL tries to reduce their effect on the quality of the solution.

Besides, the issues are modular, the same patterns appear frequently and modularity can be used to avoid learning all repeatedly.

Ordered approaches are very common for this, but performing it automatically is difficult. It is impossible to completely estimate the present state, because of the very little approaches.

Important Points in Reinforcement Learning:

  • Input – It is the starting state from where the model will begin

  • Output – In RL, there are different results available due to different solutions for a specific problem.

  • Training – Training depends on the input. The model will give a state and the user have to choose whether to give benefits or punish the model, depending on its result or outputs.

  • The model in RL learns continuously.

  • Depending on the highest benefits, we choose the best solution.

Applications of Reinforcement Learning:

  1. Used to design a training system, that gives custom instruction and materials depending on the student’s needs.

  2. It is mostly used in making Artificial Intelligence, to help in playing computer games.

  3. It is used in Robotics and automation in industries. RL allows the robot to design an effective flexible control system, for itself that understands its experience and behavior.

  4. It is applied in text description engines.

  5. RL technique of Machine Learning is used in understanding the best treatment approaches, in healthcare.

  6. RL is also used in Online Stock Trading.

In this article, I have explained about Reinforcement Learning in Machine Learning. Follow my articles, to get more updates on Machine Learning Technology. 

Also See: Reinforcement Learning, Machine Learning, Supervised Learning, Rl Technique, Rl, Reinforcement, Machine

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