Reinforcement learning

Reinforcement learning is an area of Machine Learning. Reinforcement. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of training dataset, it is bound to learn from its experience.


Supervised vs Reinforcement Learning: In supervised learning, there’s an external “supervisor”, which has knowledge of the environment and who shares it with the agent to complete the task. But there are some problems in which there are so many combinations of subtasks that the agent can perform to achieve the objective. So that creating a “supervisor” is almost impractical. For example, in a chess game, there are tens of thousands of moves that can be played. So creating a knowledge base that can be played is a tedious task. In these problems, it is more feasible to learn from one’s own experiences and gain knowledge from them. This is the main difference that can be said of reinforcement learning and supervised learning. In both supervised and reinforcement learning, there is a mapping between input and output. But in reinforcement learning, there is a reward function which acts as a feedback to the agent as opposed to supervised learning.


Unsupervised vs Reinforcement Leanring: In reinforcement learning, there’s a mapping from input to output which is not present in unsupervised learning. In unsupervised learning, the main task is to find the underlying patterns rather than the mapping. For example, if the task is to suggest a news article to a user, an unsupervised learning algorithm will look at similar articles which the person has previously read and suggest anyone from them. Whereas a reinforcement learning algorithm will get constant feedback from the user by suggesting few news articles and then build a “knowledge graph” of which articles will the person like.



Types of Reinforcement: There are two types of Reinforcement:

    Positive –
    Positive Reinforcement is defined as when an event, occurs due to a particular behavior, increases the strength and the frequency of the behavior. In other words it has a positive effect on the behavior.

    Advantages of reinforcement learning are:
        Maximizes Performance
        Sustain Change for a long period of time

    Disadvantages of reinforcement learning:
        Too much Reinforcement can lead to overload of states which can diminish the results
    Negative –
    Negative Reinforcement is defined as strengthening of a behavior because a negative condition is stopped or avoided.

    Advantages of reinforcement learning:
        Increases Behavior
        Provide defiance to minimum standard of performance

    Disadvantages of reinforcement learning:
        It Only provides enough to meet up the minimum behavior

Various Practical applications of Reinforcement Learning –

    RL can be used in robotics for industrial automation.
    RL can be used in machine learning and data processing
    RL can be used to create training systems that provide custom instruction and materials according to the requirement of students.

RL can be used in large environments in the following situations:

    A model of the environment is known, but an analytic solution is not available;
    Only a simulation model of the environment is given (the subject of simulation-based optimization);[6]
    The only way to collect information about the environment is to interact with it.