Reinforcement learning comes into AI’s mainstream

Developers now have the tools to get started with this revolutionary technology that is poised to become mainstream

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One of the most noteworthy artificial intelligence trends in 2018 has been the maturation of reinforcement learning into a mainstream approach for building and training statistical models to do useful things.

As I explained earlier this year, reinforcement learning is taking an expanding role in enterprise AI initiatives. The technique has broken out of its traditional niches in robotics, gaming, and simulation, and it is now evident in a wide range of cutting-edge AI applications in IT operations management, energy, health care, commerce, transportation, and finance. It’s even integral to a new generation of AI solutions in social media, natural language processing, machine translation, computer vision, digital assistants, and more.

To deepen the consumability of reinforcement learning algorithms in enterprise AI, developers require tools for collaborating on these projects and for deploying the resulting models into production environments. In that regard, there have been significant industry announcements recently that illustrate the maturation of open-source workbenches, libraries, and devops pipelines for reinforcement-learning-focused AI initiatives.

Iterative reinforcement-learning development workbench

Many advances in reinforcement learning are creeping into our lives either through mainstream apps we take for granted (like multiplayer online games) or use cases so futuristic (like robotics) that we don’t realize they’re creeping into the mainstream. Reinforcement-learning agents can now play games at a superhuman level, such as in the Open AI Five competition.

Developers can avail themselves of a growing range of open-source reinforcement learning frameworks for gaming and robotics, including OpenAI’s Roboschool, Unity Technology’s Machine Learning Agents, and Intel’s Nervana Coach. And you also have access to open source reinforcement-learning frameworks that are extensible to a wide range of challenges. For example, Google’s TensorFlow Agents supports efficient batched reinforcement learning workflows and UC Berkeley’s Ray RLLib provides a flexible task-based programming model for building agent-based reinforcement learning applications in TensorFlow and PyTorch.

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