Is Reinforcement Learning the Future of Machine Learning?
Machine learning is a rapidly evolving field with many new and exciting developments. Reinforcement Learning (RL) in particular has been gaining traction in recent years, and for good reason. It is the next frontier in ML and has been an area of intense interest for some time now. In this article, we will explore one of the most exciting trends in Reinforcement Learning today.
Reinforcement learning is that it requires a lot of data to work accurately and efficiently which makes it difficult for analysts or engineers to use this method extensively. Hence, reinforcement learning can be used in the following scenarios:
The environment can be simulated with enough detail so the agent doesn’t require to do random actions when interacting with an environment. In case, if there is not enough real-time data, then trained agents are effective as compared to unsupervised ML algorithms in decision making under uncertainty because they can act without any prior information of the future state of the system (environment).
Agents have been developed in real-world situations like ATARI gaming where gameplay is governed by classical physics rules and there is no human interaction. Reinforcement learning is one of the most exciting fields in AI which will be responsible for developing new techniques and systems that solve many problems facing today’s world like self-driving cars.
RL has been used widely in applications like computer gaming, robotics, etc. The most interesting aspect of RL lies in its ability to solve problems without any prior information regarding the environment or state. The main advantage of using this technique comes from its inability to solve complex nonlinear functions which are hard to fit through other methods of machine learning.
Anomaly Detection: In this type of problem, the data points which do not follow the normal domain are easily detected. This is because reinforcement learning works on both positive and negative responses. One of the best examples of anomaly detection using reinforcement learning could be its use in fraud detection. For example, if a customer purchases an expensive product or service and then makes a smaller transaction for a different product or service, this would be seen as suspicious and can be easily detected using RL.
Self-Driving Cars: The use of RL in the field of robotics is one of the most notable developments in this regard. For example, for self-driving cars to work effectively, they require a large amount of data. In this situation, RL can be used to quickly collect and process data from different driving experiences and from personal preferences like changing the speed or direction based on the response from the environment.
Bidding for online advertisements: Another real-world application is bidding for ads where we need to make decisions that depend on past history. Other applications include making investment decisions or even using RL for traffic management.
Optimization of resources (Electricity for example) to use it in an efficient way. Optimizing transportation systems like taxi services where cars follow a predetermined route and collect data about customer preferences to optimize their routes. Traffic management using reinforcement learning which requires agents who can make decisions on their own without prior knowledge of future events.
In a nutshell, RL has many applications waiting to be discovered in different fields like astronomy, biology, economics, etc. Reinforcement learning is an extremely powerful tool that can help us solve problems where no other method of machine learning could do before. There are only a few areas where RL cannot be applied due to lack of data or availability of training data but those areas would also benefit from any new techniques that may come up in the future and thus make reinforcement learning more useful than it already is!
Originally published at https://protonautoml.com on July 26, 2021.
