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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 - Second Edition Sudharsan Ravichandiran

    (ebook) (audiobook) (audiobook) Książka w języku angielskim
    Deep Reinforcement Learning with Python. Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow - Second Edition Sudharsan Ravichandiran - okladka książki

    Deep Reinforcement Learning with Python. Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow - Second Edition Sudharsan Ravichandiran - okladka książki

    Deep Reinforcement Learning with Python. Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow - Second Edition Sudharsan Ravichandiran - audiobook MP3

    Deep Reinforcement Learning with Python. Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow - Second Edition Sudharsan Ravichandiran - audiobook CD

    Autor:
    Sudharsan Ravichandiran
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    760
    Dostępne formaty:
         PDF
         ePub
         Mobi
    With 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.

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    O autorze książki

    Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

    Sudharsan Ravichandiran - pozostałe książki

    Packt Publishing - inne książki

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