Up and Running with Reinforcement Learning

What will artificial intelligence (AI) look like in the future? As  applications of AI algorithms and software become more prominent, it is a question that should interest many. Researchers and practitioners of AI face further relevant questions; how will we realize what we envision and solve known problems? What kinds of innovations and algorithms are yet to be developed? Several subfields in machine learning display great promise toward answering many of our questions. In this book, we shine the spotlight on reinforcement learning, one such, area and perhaps one of the most exciting topics in machine learning.

Reinforcement learning is motivated by the objective to learn from the environment by interacting with it. Imagine an infant and how it goes about in its environment. By moving around and acting upon its surroundings, the infant learns about physical phenomena, causal relationships, and various attributes and properties of the objects he or she interacts with. The infant's learning is often motivated by a desire to accomplish some objective, such as playing with surrounding objects or satiating some spark of curiosity. In reinforcement learning, we pursue a similar endeavor; we take a computational approach toward learning about the environment. In other words, our goal is to design algorithms that learn through their interactions with the environment in order to accomplish a task.

What use do such algorithms provide? By having a generalized learning algorithm, we can offer effective solutions to several real-world problems. A prominent example is the use of reinforcement learning algorithms to drive cars autonomously. While not fully realized, such use cases would provide great benefits to society, for reinforcement learning algorithms have empirically proven their ability to surpass human-level performance in several tasks. One watershed moment occurred in 2016 when DeepMind's AlphaGo program defeated 18-time Go world champion Lee Sedol four games to one. AlphaGo was essentially able to learn and surpass three millennia of Go wisdom cultivated by humans in a matter of months. Recently, reinforcement learning algorithms have been shown to be effective in playing more complex, real-time multi-agent games such as Dota. The same algorithms that power these game-playing algorithms have also succeeded in controlling robotic arms to pick up objects and navigating drones through mazes. These examples suggest not only what these algorithms are capable of, but also what they can potentially accomplish down the road.