Reinforcement learning (RL) has generally been applied to solve games and puzzles. From early AI applications in checkers and chess to more recent RL-based solutions that have learned how to play some of the most difficult games such as Go, DOTA, and multi-player games, RL has shown that it can offer significant strength in solving some of the more difficult challenges tasked to AI. Despite the possibilities, RL approaches are not as widely implemented in the enterprise as supervised or unsupervised learning approaches. This is because companies find the more task-oriented supervised learning approaches suitable to the recognition, conversation, and predictive analytics patterns of AI, and data-oriented unsupervised learning approaches applicable to the pattern and anomaly discovery and hyperpersonalization patterns. This leaves the goal-oriented RL approaches suitable for autonomous systems and goal-driven solutions patterns.
Despite RL’s lower enterprise profile, it has a high profile in news and media. DeepMind, acquired by Google in 2015 has been making waves with its approach to QLearning, using the RL method to beat top players at many competitive games. They see RL as a gateway to solving many general problems, and indeed, they see RL as possibly the algorithmic approach to solving the Artificial General Intelligence (AGI) challenge of a generally applicable machine learning method. While this remains to be seen, it has certainly given people much to think about, with personalities like Elon Musk, Max Tegmark, and others warning about the imminent possibility of the superintelligence.
While the fears of an imminent machine takeover is most likely overwrought, in reality, RL is being applied to much more mundane real-world activities such as resource optimization, planning, navigation, and scenario simulation approaches.
Recently Amazon has been making significant waves of their own in the RL space. At the AWS Re:Invent 2018 conference in Las Vegas last year, Amazon unveiled the DeepRacer RL platform and a league pitting individual skills to develop RL algorithms that can optimize the path of the autonomous vehicle through a controlled course. While this might seem to be a trivial application, Amazon has been at the forefront of applying RL to their own real world scenarios.