What Is Reinforcement Learning In Machine Learning?
AlphaGo from Google is an extremely powerful program – at least in its restricted area of use. AlphaGo is based on so-called reinforcement learning, a machine learning method. In this post, we want to bring you closer to reinforcement learning.
Reinforcement learning means supervised and unsupervised learning. It also describes one of the three machine learning methods. Reinforcement learning does not require any prior data, as strategies and solutions are generated on the basis of the rewards received in a so-called trial and error process.
What is reinforcement learning?
Reinforcement learning is used to find solutions and strategies for complex problems based on the trial-and-error method. Rewards are given for specific actions performed. Compared to other learning methods, no data material is needed to train the agent (the learning system). The intelligence and knowledge are therefore created concurrently during different simulation runs. The algorithms applied during this process aim to maximize the rewards obtained. Individual actions are therefore not predetermined and are determined by the utility of the rewards obtained.
The special thing about reinforcement learning is that it is very similar to human learning, for example, artificial neural networks are used for this. AlphaGo from Google can be used as a very well-known example, in which reinforcement learning is also used. The AlphaGo Zero program competes with the world’s best players of the popular Go board game and teaches itself the game without human help.
How does reinforcement learning work?
Various algorithms can be used in reinforcement learning. The system environment is changed by the actions of the agent. When applying reinforcement learning, the agent in the initial situation has no information about how a certain action will ultimately affect the system environment. In connection with a positive or negative change with regard to the problem solution, the agent receives feedback on the success of his action. These are distributed in the form of rewards and can also be absent.
In connection with the feedback received, the agent will take the next action. The algorithms always aim to maximize the rewards within the simulated system. As a result of this process, strategies and actions automatically arise or develop through which a solution is found for the problem.
The learning results map artificial neural networks in their neurons. The solution to the problem is stored in the neurons between a so-called input and output layer. The other two learning methods are called supervised and unsupervised learning.
What are the advantages of reinforcement learning?
This learning method offers several advantages over other machine methods. This process makes it possible to find solutions to complex problems without starting data and human (prior) knowledge. Reinforcement learning is very similar to the natural learning process and generates solutions that humans are not capable of. This learning method can be used for any intellectual task. In addition, the elaborate collection and processing of training methods through reinforcement learning are not necessary.
Example :
- A typical application example of supervised learning can be the recognition of persons in images.
- In addition, the automatic recognition of spam mails or handwriting recognition.
However, the creation and generation of training data in supervised learning is very complex. But even unsupervised learning needs data to work, but the difference is that these are labeled.
There are therefore no predefined solutions for the individual data sets. For this reason, the system tries to recognize different structures, patterns, and differences of the data in order to be able to group the data sets appropriately.
Reinforcement learning can find practical application, for example, in the optimization of logistics processes, in traffic light control to minimize traffic jams, in the control of air conditioning in data centers of Google, and in many other applications.
Conclusion
Reinforcement learning can and will be used in many areas in the future. In practical application, this machine learning method will make many situations easier for people and find perfect problem solutions!
