Pros and Cons of Reinforcement Learning

There are many machine learning strategies we can use, and each one of them has advantages as well as disadvantages. Today, let’s look at some pros and cons of reinforcement learning.

Before I answer that topic directly, let me try to sprinkle some basic concepts that you should know.

There are mainly three types of learning strategies in machine learning, namely, supervised learning, unsupervised learning, and reinforcement learning.

Let’s see what reinforcement learning is before we dive deep into its pros and cons.

What is Reinforcement Learning? Reinforcement learning is learning by making and correcting mistakes. 

We make a lot of mistakes. But we often try to avoid those in the future. That is how we learn, and that is how reinforcement learning works.

For example, consider the case of small babies. If they touch fire, they will feel the pain, and they will never touch fire again in their entire life knowingly.

The machine learns very complex things by making mistakes and avoiding them in the future. This process of learning is also known as the trial and error method.

In technical terms, reinforcement learning is the process in which a software agent makes observations and takes actions within an environment, and in return, it receives rewards.

Its main objective is to maximize its expected long-term rewards.

Now, let’s see the pros and cons of reinforcement learning. I have done some research on this topic, and here I put together all the important points that I found. Let’s check them out.

Pros of Reinforcement Learning

• Reinforcement learning can be used to solve very complex problems that cannot be solved by conventional techniques.
• This technique is preferred to achieve long-term results, which are very difficult to achieve.
• This learning model is very similar to the learning of human beings. Hence, it is close to achieving perfection.
• The model can correct the errors that occurred during the training process.
• Once an error is corrected by the model, the chances of occurring the same error are very less.
• It can create the perfect model to solve a particular problem.
• Robots can implement reinforcement learning algorithms to learn how to walk.
• In the absence of a training dataset, it is bound to learn from its experience.
• Reinforcement learning models can outperform humans in many tasks. DeepMind’s AlphaGo program, a reinforcement learning model, beat the world champion Lee Sedol at the game of Go in March 2016. 
• Reinforcement learning is intended to achieve the ideal behavior of a model within a specific context, to maximize its performance.
• It can be useful when the only way to collect information about the environment is to interact with it.
• Reinforcement learning algorithms maintain a balance between exploration and exploitation. Exploration is the process of trying different things to see if they are better than what has been tried before. Exploitation is the process of trying the things that have worked best in the past. Other learning algorithms do not perform this balance.

Cons of Reinforcement Learning

• Reinforcement learning as a framework is wrong in many different ways, but it is precisely this quality that makes it useful.
• Too much reinforcement learning can lead to an overload of states, which can diminish the results.
• Reinforcement learning is not preferable to use for solving simple problems.
• Reinforcement learning needs a lot of data and a lot of computation. It is data-hungry. That is why it works really well in video games because one can play the game again and again and again, so getting lots of data seems feasible.
• Reinforcement learning assumes the world is Markovian, which it is not. The Markovian model describes a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.
• The curse of dimensionality limits reinforcement learning heavily for real physical systems. According to Wikipedia, the curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience.
• Another disadvantage is the curse of real-world samples. For example, consider the case of learning by robots. The robot hardware is usually very expensive, suffers from wear and tear, and requires careful maintenance. Repairing a robot system is costs a lot.
• To solve many problems of reinforcement learning, we can use a combination of reinforcement learning with other techniques rather than leaving it altogether. One popular combination is Reinforcement learning with Deep Learning.

Honestly, it was a hard time for me to find the disadvantages of reinforcement learning, while there are plenty of advantages to this amazing technology.

If you know any advantages or disadvantages that I did not mention, feel free to comment them down below. I will add your valuable points to this article.

Difference between Reinforcement Learning and Deep Learning

The main difference between reinforcement learning and deep learning is this: Deep learning is the process of learning from a training set and then applying that learning to a new data set. But reinforcement learning is the process of dynamically learning by adjusting actions based on continuous feedback to maximize a reward.

Deep learning makes use of the existing available data and uses that data to predict patterns. Reinforcement learning can learn from its experience through trial and error.

Applications of Reinforcement Learning

A variety of problems can be solved using reinforcement learning. Some of them are game-playing, robotics, and many other fields.

As I mentioned earlier, reinforcement learning is the best technology used for game playing. It can even beat world champions.

Reinforcement learning can be used effectively to determine the best move to make in a game, depending on several different factors. It is very handy in games like Chess, Go, etc.

Using reinforcement learning, we can improve and personalize the gaming experience in real-time. It is the algorithm that can solve different games and sometimes achieve super-human performance.

This technology is used for the learning of robots. Robots are trained using the trial and error method with human supervision. Reinforcement learning teaches robots new tasks while retaining prior knowledge.

E-commerce websites like Amazon can use reinforcement learning to solve their problems to generate the maximum revenue by displaying the most relevant ads to interested buyers.

Self-driving cars also implement some reinforcement learning algorithms. Reinforcement learning can also be applied to optimizing chemical reactions.

Industrial automation, trading stock prices forecasting, news recommendations, etc. are some other applications of reinforcement learning. Check out this article to learn more about the applications of reinforcement learning.

Conclusion

Reinforcement learning is a type of machine learning in which the machine learns by itself after making many mistakes and correcting them.

This learning strategy has many advantages, as well as some disadvantages. I found it hard to find more than a few disadvantages of reinforcement learning.

If you’re a beginner in machine learning, check out the article I’ve written on Introduction To Machine Learning.

If you have any doubts, or points to add, feel free to mention them in the comments section.

Share this article if you find it useful so that it will help someone else as well.

Happy learning.