Applications of Reinforcement Learning

In reinforcement learning, an agent learns how to successfully reach a goal within a dynamic and potentially complex environment. This subdivision of machine learning is a behavioral learning model concerned with how a system should behave (or take action) within a state of an environment to earn rewards that could be crucial to the system's progress to another state of the environment. This technique adopts a feedback system of learning in which the algorithm is given information about whether its choices are accurate or not to lead the user to the best possible outcome.

The following are some applications of reinforcement learning:

Self-Driving Cars

In this application, reinforcement learning could be put to tasks such as dynamic pathing, motion planning, controller optimization, etc. Suppose an autonomous vehicle were to successfully overtake another vehicle. In that case, a learning policy of overtaking could be established to not only complete this task by avoiding collision but also returning to a safe speed. Automatic parking policies can make parking possible. In practicalexamples, companies such as have recorded success in the use of reinforcement learning to train a car to drive.

Traffic Control

Based on the results from a paper published as “Reinforcement Learning Based Multi-Agent System for Network Traffic Control,” the testing of a traffic light controller to solve traffic congestion problems within a simulated environment proved to be a better method than traditional methods currently used to control traffic. Research like this opens the door to the potential application of multi-agent reinforcement learning in designing traffic systems.


Within the field of robotics, reinforcement learning may establish itself as a method with widespread applications. A robot may be trained to learn policies through which it can map raw video images to robot actions to generate motor torques as output after the processing of the images througha convolutional neural network.

Web System Configuration

With at least 100 configurable parameters in a web system typically requiring the expertise of skilled operators and multiple trial-and-error approaches; reinforcement learning has been demonstrated to be of use in automating this process as shown by the paper, “A Reinforcement Learning Approach to Online Web System Auto-configuration.” This paper marks the first-ever attempt within the domain to carry out the autonomic reconfiguration of parameters in a multi- tier web system in a VM-based dynamic environment.


Reinforcement learning has been successfully deployed to the optimization of chemical reactions, surpassing (performance-wise) other advanced algorithms used for this purpose and generalizing well to dissimilar underlying mechanisms demonstrated in the paper, “Optimizing Chemical Reactions with Deep Reinforcement Learning”.

Personalized Recommendations

Although initial problems were encountered in earlier news recommendations for reasons as varied as the human tendency to get bored, the evolvingnature of news, and Click-Through Rate not paint an accurate picture of users' retention rate. However, through work done by Guanjie et. al. in which four category features namely user features, context-features (corresponding to state features of the environment), user-news features and news features (as the action features) were applied; some of these problems faced by recommendation systems were tackled.


Reinforcement learning has become the technique of choice for generating peak performance in games and solving different games. By combiningneural networks (which have achieved much success in areas like machine translation and computer vision) with reinforcement learning; powerfulprograms like the famous AlphaGo by Deep Mind have been created. This program is popular for having defeated the best players in the world at Go.

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