Reinforcement learning is a type of AI that is changing how businesses solve problems with simulations. But why is it happening now? AI has dominated tech headlines for almost a decade. We’ve seen AI applied to everything from autonomous vehicles to better healthcare diagnoses. But with reinforcement learning, or RL, AI is finally learning how to behave strategically to meet business goals. 

Why Reinforcement Learning?

Reinforcement learning simply means that the RL algorithm rewards good actions and penalizes bad ones, according to how well they serve your goals. Reinforcement learning is setting records that were once thought impossible and breaking down barriers to better prediction and planning.

AI think tanks like DeepMind and OpenAI have repeatedly shown that reinforcement learning can win complex games and beat world champions at everything from the board game of Go to Dota2. Those breakthroughs have drawn the world’s attention to the potential for reinforcement learning to help make better decisions, and master a different set of challenges. 

Because games are just simulations, or virtual environments that allow you to act in complex systems. And simulations are widely used in business to model expensive decisions before taking them, and to emulate complex environments in order to understand them more deeply. 

These complex games mirror simulation models which are widely used in business for decision making. Simulations, or artificial environments for action, allow companies to model complex environments before making expensive decisions for increased understanding and, ultimately, better decision-making.

Why Reinforcement Learning for Business Decisions?

Boxes on a conveyor system

New solutions are needed now more than ever. Three-quarters of all businesses surveyed in April 2020 predicted that they would face supply chain disruptions due to the Coronavirus pandemic and subsequent shutdowns. Many restaurants were forced to shut down for months, and as the foodservice industry slowed down, it impacted their upstream suppliers. Reinforcement learning is one way to explore the best responses to sudden shifts in data, such as supply and demand shocks

Running an optimizer within a simulation means finding the best decisions to achieve your goal, just like running AI in a video game means finding new methods to win. Businesses are modeling problems that include factory configuration, machine behavior, supply chain routing, and resource allocation. These problems are all “games” that a business can “win” by setting the right goals. The goals are to improve performance, or for example, produce goods more efficiently, deliver products more quickly, or reallocate resources to meet shifts in demand.

Reinforcement Learning Versus Traditional Optimizers

Traditional optimizers work well in solving for static environments. As simulations grow more complex, and as they confront unexpected shifts in supply, demand, and other conditions, traditional mathematical solvers need to be rewritten and recalculated, which is time consuming. Some problems they face have no analytical solution. Reinforcement learning is the perfect tool to solve a new set of problems, problems that more closely resemble real factories and supply chains in the changing economic environment. 

Reinforcement learning is the way AI transforms both simulation and businesses’ operational decision making. Other types of AI classify objects and recognize patterns. Reinforcement learning predicts how you should behave to succeed. It is goal-oriented AI, and it is capable of finding the best path in complex business scenarios. 

Reinforcement learning will allow businesses to be AI-driven for the first time because it makes a direct impact on the bottom line. This type of AI is not just a cool feature. It’s the control center of your operations. It’s an algorithm that can help you hit your milestones. 

Conclusions and Next Steps

Reinforcement learning is a kind of AI that is used to find the best decisions in complex virtual environments, like games and simulations. It can optimize actions within a simulation to help achieve your goals. The algorithms that have been used to win complex games like Go and Starcraft have everything they need to optimize business operations. The “points” in simulations are just business metrics, and the actions are operational decisions rather than moves in a video game. Reinforcement learning algorithms can find good decision points in complex simulations that other solvers can’t tackle. 

Adapting your simulation to reinforcement learning is an additional step, but with a Web-based approach and a platform that takes the guesswork out of AI, it’s easy to get results quickly. 

Learn more about applying reinforcement learning to your specific business problems on our Industries page.