The applicability of RL in the enterprise is vast and largely untapped. To date, most Deep Reinforcement Learning successes have focused on its application to games and robotics. In such cases, emulators and simulators are readily available and present the perfect environment in which to run trials without risk. By contrast, many of the problems that companies wish to solve do not come with a risk-free testing environment: It can be difficult and sometimes impossible to allow an AI agent to freely and rapidly explore the impact of its potential actions through trial and error. But the availability of a simulator is not essential to effectively applying RL techniques in enterprise settings.

The applicability of RL in the enterprise is vast and largely untapped. To date, most Deep Reinforcement Learning successes have focused on its application to games and robotics. In such cases, emulators and simulators are readily available and present the perfect environment in which to run trials without risk. By contrast, many of the problems that companies wish to solve do not come with a risk-free testing environment: It can be difficult and sometimes impossible to allow an AI agent to freely and rapidly explore the impact of its potential actions through trial and error. But the availability of a simulator is not essential to effectively applying RL techniques in enterprise settings.