Deep Reinforcement Learning HandsOn
Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition
by Maxim Lapan
Should I read this?
Recommended by 1 source and appears in Machine Learning and Data Science.
New edition of the bestselling guide to deep reinforcement learning and how it's used to solve complex realworld problems. Revised and expanded to include multiagent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more Key Features Second edition of the bestselling introduction to deep reinforcement learning, ...
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Why recommended
Recommended by 1 source and appears in Machine Learning and Data Science.
Recommended by notable people
People and public figures who have recommended this book.
Recommendation Signals
Recommendation proof is sourced from public posts, interviews, reading lists, and cited references.
Kirk Borne
“Introduction to various #ReinforcementLearning #Algorithms: ————— #BigData #DataScience #AI #MachineLearning #DeepLearning #DataMining #Mathematics #abdsc ————— ++See this book:”
Appears In
Not sure if this is the right fit?
Consider Life 3.0 by Max Tegmark. Recommended by 18 sources.
“Life 3.0 reads like a long, wide-ranging conversation with a physicist who loves big if-then thought experiments. The useful part is its panoramic sweep across possible AI futures—from job automation to cosmic colonization—forcing you to consider timelines you might otherwise avoid. The limitation is that the speculative breadth often outruns the depth; chapters can feel meandering, and some readers will find the cosmic-scale scenarios too detached from practical concerns, making it hard to ground in real urgency.”
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SegaranHow recommendation signals are reviewed
Each recommendation is collected from a public source — interviews, articles, or curated lists — and linked to its original URL. Books with many verifiable recommendations from respected people rank higher.