Artificial intelligence (AI) is expanding rapidly, with an estimated market size of USD 7.35 billion. Nowadays, AI continually influences all aspects of our daily lives. Therefore, lots of tech companies are building state-of-the-art AI-powered cybersecurity defense solutions that are designed and programmed by penetration testers and ethical hackers.
Machine learning (ML) and deep learning (DL) are two artificial intelligence solutions that are diversified in nature. There are various subtypes to these types of learning such as supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning.
In this article, we are going to focus on reinforcement learning, diving into what reinforcement learning entails and how you should apply it in your AI endeavors.
What Is Reinforcement Learning?
Reinforcement learning is the process of training machine learning models to make a sequence of decisions. This then helps the agent to learn how to achieve a goal in an uncertain and complex environment. Artificial intelligence faces a game-like situation in reinforcement learning.
The computer will use a trial-and-error method to come up with a solution to a problem. A programmer can get a machine to do what he or she wants by either rewarding or giving penalties to the AI for each action that it performs. This then helps the artificial intelligence to maximize the total reward.
Important Terms Used in the Deep Reinforcement Learning Method
Some of the important terms that are used in reinforcement learning are:
Agent: This is an assumed entity that performs an action in an environment to get rewards.
Environment: This is the scenario that an agent faces.
State: This is the current situation that is returned by the environment.
Reward: This is the immediate return that an agent gets once he or she performs a specific action.
Value: This is an expected long-term return with a discount when compared with a short-term reward.
Policy: This is a strategy used by the agent to make a decision on its next action according to the current state.
Model of the environment: This usually mimics the behavior of the environment. It will then help the programmer to make inferences and determine the way an environment will behave.
Q value: This is mostly the same as value. However, the Q value helps to take additional parameters as current actions.
Main Points in Reinforcement Learning
Some of the major points used in reinforcement learning are:
Input: This is the initial state a model will start from.
Output: There are usually lots of possible outputs because there are several solutions to a particular problem.
Training: This is usually based on the input. This is because the model will come back with a state and the user can then decide to reward or punish the model according to its output. The model will continue to learn and the best solution will be decided according to the maximum reward.
Types of Reinforcement Learning
There are two types of reinforcement:
Positive reinforcement happens when an event occurs because of a particular behavior that increases the strength and frequency of the behavior. This means that it has a positive effect on the behavior.
Advantages of Positive Reinforcement
- Helps to sustain change for a long time.
- Helps to maximize performance.
Disadvantages of Positive Reinforcement
- This can lead to an overload of states that will diminish the result if the reinforcement is too much.
This is the process where a particular behavior is strengthened because a negative condition was avoided or stopped.
Advantages of Negative Reinforcement
- It helps agents to refuse the minimum performance standard.
- It increases behavior.
Disadvantages of Negative Reinforcement
- It can only provide enough for an agent to meet up with the minimum behavior.
Applications of Reinforcement Learning
- You can use it in machine learning and data processing.
- You can use it in robotics for industrial automation.
- Can be used for creating training systems that have custom instructions and materials based on the requirement of students.
- Can be used in large environments in situations like:
- When you can only gather information about an environment by interacting with it.
- If you only know the model of an environment but the analytic solution is not available.
- If you are given only the simulation model of the environment.
Reinforcement Learning Algorithms
There are three approaches that programmers can use to implement a reinforcement learning algorithm.
You should always try to maximize the value function V(s) while using the method. Furthermore, the agent will expect a long-term return of the current states under policy π.
In this method, you will try to propose a policy that the action which is performed in every state will help the agent to get the maximum reward in the future. There are two types of policy-based methods that you can use, namely:
In this method, you will design a virtual model for every environment. The agent will then learn to perform in that specific environment on their own.
Learning Models of Reinforcement
The two important learning models used in reinforcement learning are:
- Markov Decision Process
- Q learning
Understand Reinforcement Learning on a Deeper Level
Reinforcement learning is a crucial artificial intelligence paradigm shift because it creates a path for AGI, from the finance industry to robotics, and it will play a major role in shaping the future of AI. EC-Council CodeRed’s “Reinforcement Learning Course” will teach you everything you need to know about reinforcement learning, whether it’s understanding building blocks and frameworks or playing games using RL.
By the end of the course, you will know how RL/AI agents are trained to perform both simple and complex tasks. You will also learn how to build AI agents easily on your own.