Deep reinforcement learning in the enterprise: Bridging the gap from games to industry

Mark Hammond explores how enterprises can move beyond games and leverage deep reinforcement learning and simulation-based training to build programmable, adaptive, and trusted AI models for their real-world applications.

Reinforcement learning is an increasingly popular machine learning technique that is particularly well suited for addressing problems within dynamic and adaptive environments. When paired with simulations, reinforcement learning is a powerful tool for training AI models that can help increase automation or optimize operational efficiency of sophisticated systems such as robotics, manufacturing, and supply chain logistics. However, moving from the games commonly used to demonstrate these techniques into real-world applications isn’t always straightforward. Structuring solutions to move beyond purely data-driven training introduces all sorts of new complexity, requiring you to consider things like how to use simulations to target your learning objectives, what kinds of simulations are applicable, how to deal with long-running simulations, how to incorporate ongoing training refinement once deployed, how to account for scaling and performance, and ultimately how to bridge from simulation to the real world. Mark Hammond explores these considerations using real-world use cases and shares lessons learned and best practices so that you can effectively leverage reinforcement learning in your own applications.