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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Deep Reinforcement Learning with Python - Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow, 2nd Edition (Paperback, 2nd Revised edition)
Loot Price: R1,412
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Deep Reinforcement Learning with Python - Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow, 2nd Edition (Paperback, 2nd Revised edition)
Expected to ship within 10 - 15 working days
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An example-rich guide for beginners to start their reinforcement
and deep reinforcement learning journey with state-of-the-art
distinct algorithms Key Features Covers a vast spectrum of
basic-to-advanced RL algorithms with mathematical explanations of
each algorithm Learn how to implement algorithms with code by
following examples with line-by-line explanations Explore the
latest RL methodologies such as DDPG, PPO, and the use of expert
demonstrations Book DescriptionWith significant enhancements in the
quality and quantity of algorithms in recent years, this second
edition of Hands-On Reinforcement Learning with Python has been
revamped into an example-rich guide to learning state-of-the-art
reinforcement learning (RL) and deep RL algorithms with TensorFlow
2 and the OpenAI Gym toolkit. In addition to exploring RL basics
and foundational concepts such as Bellman equation, Markov decision
processes, and dynamic programming algorithms, this second edition
dives deep into the full spectrum of value-based, policy-based, and
actor-critic RL methods. It explores state-of-the-art algorithms
such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth,
demystifying the underlying math and demonstrating implementations
through simple code examples. The book has several new chapters
dedicated to new RL techniques, including distributional RL,
imitation learning, inverse RL, and meta RL. You will learn to
leverage stable baselines, an improvement of OpenAI's baseline
library, to effortlessly implement popular RL algorithms. The book
concludes with an overview of promising approaches such as
meta-learning and imagination augmented agents in research. By the
end, you will become skilled in effectively employing RL and deep
RL in your real-world projects. What you will learn Understand core
RL concepts including the methodologies, math, and code Train an
agent to solve Blackjack, FrozenLake, and many other problems using
OpenAI Gym Train an agent to play Ms Pac-Man using a Deep Q Network
Learn policy-based, value-based, and actor-critic methods Master
the math behind DDPG, TD3, TRPO, PPO, and many others Explore new
avenues such as the distributional RL, meta RL, and inverse RL Use
Stable Baselines to train an agent to walk and play Atari games Who
this book is forIf you're a machine learning developer with little
or no experience with neural networks interested in artificial
intelligence and want to learn about reinforcement learning from
scratch, this book is for you. Basic familiarity with linear
algebra, calculus, and the Python programming language is required.
Some experience with TensorFlow would be a plus.
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