Reinforcement learning is a machine learning subcategory used to teach software programs effective decision-making in interactive environments. The program is trained through a trial-and-error approach by observing its environment and making decisions based on positive or negative feedback.
Software developers design the reinforcement learning model to help the software program achieve the maximum reward in each presented scenario. This helps the software model improve over time and achieve reliable performance levels.
Reinforcement learning has been applied to various industries such as automatic trading in finance and optimized treatment regimes in healthcare. The algorithms start by retrieving and analyzing data from an IT infrastructure resource, such as a data center or cloud platform, where the appropriate action is then implemented and stored. Due to reinforcement learning’s ability to apply trial and error in these use cases, organizations can improve development process and increase customer engagement.