Yes, I have been wondering if computer programs could learn to make decisions and how exactly do they work? That is where reinforcement learning takes the lead! In this blog post, we’re going to explore the diverse types of reinforcement learning algorithms. Do not get overwhelmed if it sounds complex; we’ll describe it in an easy way, using simple language. Hence, sit back, grab a snack, and let’s dive into the fascinating world of reinforcement learning algorithms!]
Want to know what reinforcement learning is? Let’s deal with that at the very beginning. What is reinforcement learning exactly? Assume that you want to train a puppy. Each time it listens to your command you reward it. This concept of rewards is critical in reinforcement learning. The algorithm is taught to choose actions depending on the rewards it gets. A positive reward means it has done something right and hence gets a positive reward, while a negative one will be given to it if it does something the wrong way. It is the way we learn from experiences in the first place!
So, let us think about reinforcement learning as intellectual play. The algorithm, like the puppy, is coping out the best moves to make because they are the ones that will eventually win. There are different ways to play this game, and this leads us to the types of algorithms. A basic understanding of these types will make it easier for you to see the ways computers adapt to different environments. Are you set to learn all of them?
1. Understanding Reinforcement Learning
First, let’s start the explanation with reinforcement learning which is a way to conceptualize it. Reinforcement learning is the process of the algorithm which creates a model of the behavior and from this model, it can understand the principle of action choosing. A new concept to think about here is: Imaging training a puppy. After every order, the puppy receives a tasty treat. This idea of rewarding is the core of reinforcement learning. The algorithm is programmed to take actions that depend on the rewards it receives. In case it has done something good it can get a positive reward while in case it has done something wrong it may even receive a negative one. This is the way we learn from experiences in the first place!
Suppose reinforcement learning were a game; the algorithm would be the puppy trying to figure out the best steps to win. There are different techniques for playing this game and hence, there are different algorithms that can be used. Knowing these will help you understand how computers learn in various environments. Are you ready for a new world of algorithms?
2. Value-Based Algorithms
Let us get it started with the value-based algorithms. Typically, these are the most popular reinforcement learning algorithms. They are meant to figure out which actions give you the highest score. Let’s go with the example of a game again — the one where your goal is to find the best move to make. Q-learning algorithms are a representative example of these type algorithms. They work through creating a table to remember the outcomes of the actions based on the past experiences.
The methods here mainly focus on the way agents enrich their knowledge, which is by getting more and more information. The process of doing this quite often involves the use of a method called the Bellman equation. The concepts of the word may deter you from the true nature of the process. To say it plainly, it means, it is just a technique to look ahead to the future and calculate the rewards. Moving on, agents can learn and be better at the process of decision-making over time. Is not that fascinating?
3. Policy-Based Algorithms
Let’s switch a gear and talk about policy-based algorithms for a few moments. Policy-learning is a new method. With it, the algorithms are no longer trying to figure out the future rewards; instead, they are taught directly a policy – a strategy for taking action. Playing basketball can be used as an example of these algorithms but thinking about the successful three points is the easiest way to understand them!]
Policy gradients are a type of algorithms, which the users can enjoy. They are so cool to be implemented in terms of these. The next that they do is to alter the policy with the goal of increasing the rewards. In reality, these types of algorithms have the great advantage of being able to cope better with complicated actions than value-based methods. Thus, in more complex situations, they seem to be the better option for handling them.
4. Actor-Critic Methods
Do you know any actor-critic methods? Well, let me tell you that they are amazing algorithms that have the characteristics of two things at the same time! They, in fact, incorporate both value-based and the policy-based type. The “actor” performs the task of selecting actions, while the “critic” evaluates them. It will be like having a coach on your team, judging your plays!
Using an actor along with a critic yields a more effective learning process for these algorithms. They can blur the distinction between a known world and a new one. For that reason, they come in handy for a broad range of tasks. What shines the most out of these actor-critic models is they can be used in the majority of practical cases, from robotics to gaming. Isn’t that immensely appealing?
Read more: Power of Reinforcement Learning
5. Deep Reinforcement Learning
If we make a considerable jump into deep reinforcement learning, we might come to the knowledge that this field combines reinforcement learning with deep learning techniques. These days, the forms of tables to represent knowledge are no longer valid, so step into the world of deep learning and try to understand neural networks as the brain of the algorithm
Artificial neural networks (ANNs) are used in place of memory tables to solve the problem of representing knowledge in the artificial brain. Neural networks allow the brain to learn from the experiences of the body or any other experiences of the mind.
The combination of deep reinforcement learning and neural networks has been a hot topic in the AI field lately. This type of algorithm has become able to solve tasks that were initially thought to be impossible. Namely, using it to train AI to play games at the level of superhuman! Deep reinforcement learning, which is now the one that can think about deep learning, can have the data analyzed deeper and hence come up with patterns to make better decisions. Long-term use will be the greatest for deep reinforcement learning!
6. Model-Based Reinforcement Learning
We will now examine the model-based reinforcement learning. The idea here is that the algorithm begins to create a model of the environment it interacts with. We can conceive it as a situation when a pilot is flying an aircraft. He studies the flight path before taking off trying to foresee the possible challenges. Similarly, model-based algorithms are those algorithms that among other things, besides the data they collect, are those that predict the outcomes based on their understanding of the environment.
The speed of learning in this way is usually faster due to the fact that the algorithm is able to try out different scenarios without physically existing in these. Another way to learn faster is to use a more accurate model. Sometimes modeling can be erroneous, and that is when a defective model can lead to poor choices made by an algorithm. However, if used correctly, model-based reinforcement learning can be amazing.
7. Multi-Agent Reinforcement Learning
Remember how it feels to play a multiplayer video game? In this situation, multiple agents can learn from each other. This is based on the idea of multi-agent reinforcement learning. This kind of learning includes the multiple agents’ actions of interacting, and competing with each other.
These agents provide us a unique insight into complex systems. For instance, let us think of a situation where traffic is run by different cars (agents) and each car learns to navigate by the others’ moves. Both collaboration and competition are the ways that different learning outcomes come about. Multi-agent reinforcement learning is a rapidly growing area in robotics and social sciences, where interactions are of utmost importance.
8. Inverse Reinforcement Learning
Have you ever considered, trying to learn from someone else’s action? The concept of inverse reinforcement learning may be new to you. Algorithm learns by just observing an expert instead of getting rewards and models of the situation through interactions with the environment. It means that the algorithm infers from the experts what will be the actions and decisions by which the experts are directed and which give them success. It’s almost like shadowing a professional to understand their craft!
Inverse reinforcement learning is particularly suitable when defining rewards is extremely difficult. This fact is given as an example: Teaching a robot the skills of cooking is one of the examples which could be a problem. But by mimicking a human chef, the robot can learn the right techniques without explicitly being told what to do. Grasping from the best without being told explicitly how to do the job is a very interesting part of learning.
9. Hierarchical Reinforcement Learning
Let us now focus on hierarchical reinforcement learning. In this type of learning, difficult tasks are broken down into smaller and more understandable ones. To illustrate, if you have a cake in the oven, rather than taking the whole process into account, you could focus on curing the batter first and then move on to the next steps, like baking and finally decorating. Come on, learn the nitty-gritty of it!
These algorithms can now learn from easily structured tasks. In this way, it can get rid of such a load as learning everything at once. Just as learning to cook a meal one step at a time, these algorithms are taught in a step-by-step approach that leads to the successful completion of the task.
10. The Future of Reinforcement Learning
We have accomplished the journey, now we can imagine the possibilities for the future. With the continuous progress in technology, we can anticipate that additional forward-looking algorithms will be introduced. Such approaches may involve advanced robotics, and even personalized learning software can be invented. The vistas are infinite!
As regards the adaptation of the new technology nowadays art is unceasingly intentioned. If we get closer to the point of operational AI, then AI will genuinely play its own way with the humans at a cognition level that is the same as humans. Better to tell you the truth, maybe one day we will become some of the most intriguing of God’s creatures.
Conclusion: Embracing the Power of Reinforcement Learning
In summary, we made an exhaustive inspection of reinforcement learning algorithms. The value-based to deep learning methods are the specifics of each sort of nature and domain. The growth of technologies will bring more advanced techniques in these methods which, in their turn, will lead to the AI future. It is my wish that this blog has brought out the idea of reinforcement learning clearly for you! Look forward to other learning experiences in the future, and just imagine all the things you are going to stumble upon!]