What is Reinforcement Learning in Machine Learning

Reinforcement learning has been used to train self-driving cars and teach robots how to grasp different objects in novel situations, among other applications. What exactly is reinforcement learning? How does it differ from machine learning? And what are some real-world examples of RL in action? Let’s explore the answers to these questions and more in this guide to reinforcement learning.

What is Reinforcement Learning?

Reinforcement learning (RL) is a subfield of machine learning that involves teaching computers how to learn from experience by assigning them rewards and punishments in response to their actions.

These rewards or feedback can be in many forms, such as rewards or penalties (positive or negative | good or bad), and are typically used to motivate the agent towards desired behaviors.

In technical terms, RL is a method of teaching machines to learn from data. In general, it is a type of artificial intelligence where computers receive “rewards” or “points” for correct behavior and then adjust their behavior accordingly.

By repeating this process over and over, an AI system will gradually learn how to perform tasks without being explicitly programmed to do so.

You can relate RL to our human life. For example, think about the situation of young newborns. If they come into contact with fire, they will suffer the pain, and they will never intentionally come into contact with fire again in their lives.

Reinforcement Learning example

reinforcement-learning-example-dog-catching-ball
Reinforcement Learning example dog catching a ball

Let’s look at some easy examples that will help you understand the reinforcement learning workflow.

Consider the scenario of teaching your dog to catch a ball. Here, the dog is an agent and he is exposed to an environment where he needs to learn how to catch a ball.

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As a dog doesn’t understand English or any other human language, you can not instruct him directly on what to do. Instead, you need to follow a different strategy.

We need to recreate a training session where the dog will try to respond in different ways. If the dog responds in the desired manner, we will reward him with a chew bone (food).

When the dog is exposed to the same situation again, he does a similar action with more energy in the hope of receiving more reward (chew bone).

This is how a dog learns to do a new thing from positive experiences.

At the same time dog will learn what not to do when confronted with a negative experience.

Supervised Learning vs Reinforcement Learning

The main difference between RL and supervised learning or deep learning is: Supervised learning or deep learning or machine learning is the process of learning from training data set and then applying the trained model to a new dataset or test data set. On the other hand, RL is the process of learning dynamically by changing actions based on continuous feedback in order to maximize a reward.

Deep learning predicts patterns with the help of available input data. RL learns from the experience through trial and error.

Types of Reinforcement Learning

There are mainly two types of RL:

1. Positive Reinforcement Learning

It is described as an event that occurs as a result of certain behavior. It improves the power and frequency of the behavior and has a beneficial influence on the action taken by the agent.

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This sort of Reinforcement enables you to maximize performance and hold changes for a longer period of time. However, too much Reinforcement may result in over-optimization of the state, that can affect the outcomes.

2. Negative Reinforcement Learning

Negative Reinforcement is defined as giving confidence in behavior that occurs because of a negative condition which needs to be stopped or avoided.

This kind of Reinforcement helps you to minimize the performance. The disadvantage of this technique is that it just gives enough to fulfill the bare minimum behavior.

Reinforcement Learning Algorithms

There are three methods for implementing a Reinforcement Learning algorithm.

1. Value Based

The main objective of this type of RL algorithm is to maximize a value function. Here, an agent expects a long-term outcome of the current state through policy.

2. Policy Based

In a policy-based RL technique, you strive to build a policy in which every action taken in each state helps you obtain maximum reward in the future.

There are two kinds of policy-based techniques:

  • Deterministic: The policy produces the same action in every state
  • Stochastic: Every action has a specific probability

3. Model Based

You must create a virtual model for each environment in this RL approach. The agent learns to perform in that particular environment.

Popular Models for Reinforcement Learning

In RL, there are two main learning models:

  • Markov Decision Process (MDP): The backbone of the Markov Decision Process (MDP) is to map solutions.
  • Q learning: It is a value-based RL model to supply information to inform which action an agent should take

Application for Reinforcement Learning

RL can be used to solve a variety of problems.

  • Automatic game playing
  • Robotics for industrial automation
  • Internet of Things (IOT)
  • Autonomous Self Driving car
  • etc.
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Python Reinforcement learning libraries

There are lots of open source libraries available to implement RL in Python like:

  • KerasRL
  • Tensorforce
  • Pyqlearning
  • etc.

Conclusion

Reinforcement learning is a sort of machine learning in which the system learns by making and fixing mistakes on its own.

This learning approach has both advantages and disadvantages. I couldn’t think of more than a few drawbacks to reinforcement learning.

If you are new to computer vision, checkout the article I have written on Real-time face detection using Python and OpenCV

If you have any questions or suggestions regarding this article, feel free to point them in the comment section below.

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