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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Mastering Reinforcement Learning with Python - Build next-generation, self-learning models using reinforcement learning techniques and best practices (Paperback)
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Mastering Reinforcement Learning with Python - Build next-generation, self-learning models using reinforcement learning techniques and best practices (Paperback)
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Get hands-on experience in creating state-of-the-art reinforcement
learning agents using TensorFlow and RLlib to solve complex
real-world business and industry problems with the help of expert
tips and best practices Key Features Understand how large-scale
state-of-the-art RL algorithms and approaches work Apply RL to
solve complex problems in marketing, robotics, supply chain,
finance, cybersecurity, and more Explore tips and best practices
from experts that will enable you to overcome real-world RL
challenges Book DescriptionReinforcement learning (RL) is a field
of artificial intelligence (AI) used for creating self-learning
autonomous agents. Building on a strong theoretical foundation,
this book takes a practical approach and uses examples inspired by
real-world industry problems to teach you about state-of-the-art
RL. Starting with bandit problems, Markov decision processes, and
dynamic programming, the book provides an in-depth review of the
classical RL techniques, such as Monte Carlo methods and
temporal-difference learning. After that, you will learn about deep
Q-learning, policy gradient algorithms, actor-critic methods,
model-based methods, and multi-agent reinforcement learning. Then,
you'll be introduced to some of the key approaches behind the most
successful RL implementations, such as domain randomization and
curiosity-driven learning. As you advance, you'll explore many
novel algorithms with advanced implementations using modern Python
libraries such as TensorFlow and Ray's RLlib package. You'll also
find out how to implement RL in areas such as robotics, supply
chain management, marketing, finance, smart cities, and
cybersecurity while assessing the trade-offs between different
approaches and avoiding common pitfalls. By the end of this book,
you'll have mastered how to train and deploy your own RL agents for
solving RL problems. What you will learn Model and solve complex
sequential decision-making problems using RL Develop a solid
understanding of how state-of-the-art RL methods work Use Python
and TensorFlow to code RL algorithms from scratch Parallelize and
scale up your RL implementations using Ray's RLlib package Get
in-depth knowledge of a wide variety of RL topics Understand the
trade-offs between different RL approaches Discover and address the
challenges of implementing RL in the real world Who this book is
forThis book is for expert machine learning practitioners and
researchers looking to focus on hands-on reinforcement learning
with Python by implementing advanced deep reinforcement learning
concepts in real-world projects. Reinforcement learning experts who
want to advance their knowledge to tackle large-scale and complex
sequential decision-making problems will also find this book
useful. Working knowledge of Python programming and deep learning
along with prior experience in reinforcement learning is required.
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