Reinforcement learning is a powerful tool in artificial intelligence in
which virtual or physical agents learn to optimize their decision making
to achieve long-term goals. In some cases, this machine learning
approach can save programmers time, outperform existing controllers,
reach super-human performance, and continually adapt to changing
conditions. This book argues that these successes show reinforcement
learning can be adopted successfully in many different situations,
including robot control, stock trading, supply chain optimization, and
plant control. However, reinforcement learning has traditionally been
limited to applications in virtual environments or simulations in which
the setup is already provided. Furthermore, experimentation may be
completed for an almost limitless number of attempts risk-free. In many
real-life tasks, applying reinforcement learning is not as simple as (1)
data is not in the correct form for reinforcement learning, (2) data is
scarce, and (3) automation has limitations in the real-world. Therefore,
this book is written to help academics, domain specialists, and data
enthusiast alike to understand the basic principles of applying
reinforcement learning to real-world problems. This is achieved by
focusing on the process of taking practical examples and modeling
standard data into the correct form required to then apply basic agents.
To further assist with readers gaining a deep and grounded understanding
of the approaches, the book shows hand-calculated examples in full and
then how this can be achieved in a more automated manner with code. For
decision makers who are interested in reinforcement learning as a
solution but are not technically proficient we include simple,
non-technical examples in the introduction and case studies section.
These provide context of what reinforcement learning offer but also the
challenges and risks associated with applying it in practice.
Specifically, the book illustrates the differences between reinforcement
learning and other machine learning approaches as well as how well-known
companies have found success using the approach to their problems.
