Deep Reinforcement Learning with Python – Second Edition

Deep Reinforcement Learning with Python, 2nd Edition

eBook Details:

  • Paperback: 760 pages
  • Publisher: WOW! eBook (September 30, 2020)
  • Language:¬†English
  • ISBN-10: 1839210680
  • ISBN-13: 978-1839210686

eBook Description:

Deep Reinforcement Learning with Python, 2nd Edition: An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms

With significant enhancements in the quality and quantity of algorithms in recent years, this Hands-On Reinforcement Learning with Python, Second Edition 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 Deep Reinforcement Learning with Python, Second Edition 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 Hands-On Reinforcement Learning with Python, 2nd Edition book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research.

  • 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

By the end of the Deep Reinforcement Learning with Python, 2nd Edition book, you will become skilled in effectively employing RL and deep RL in your real-world projects.


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