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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

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

E-book
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.
  • 1. Fundamentals of Reinforcement Learning
  • 2. A Guide to the Gym Toolkit
  • 3. The Bellman Equation and Dynamic Programming
  • 4. Monte Carlo Methods
  • 5. Understanding Temporal Difference Learning
  • 6. Case Study – The MAB Problem
  • 7. Deep Learning Foundations
  • 8. A Primer on TensorFlow
  • 9. Deep Q Network and Its Variants
  • 10. Policy Gradient Method
  • 11. Actor-Critic Methods – A2C and A3C
  • 12. Learning DDPG, TD3, and SAC
  • 13. TRPO, PPO, and ACKTR Methods
  • 14. Distributional Reinforcement Learning
  • 15. Imitation Learning and Inverse RL
  • 16. Deep Reinforcement Learning with Stable Baselines
  • 17. Reinforcement Learning Frontiers
  • 18. Appendix 1 – Reinforcement Learning Algorithms
  • 19. Appendix 2 – Assessments
  • Titel: Deep Reinforcement Learning with Python. Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow - Second Edition
  • Autor: Sudharsan Ravichandiran
  • Originaler Titel: Deep Reinforcement Learning with Python. Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow - Second Edition
  • ISBN: 9781839215599, 9781839215599
  • Veröffentlichungsdatum: 2020-09-30
  • Format: E-book
  • Artikelkennung: e_2ag5
  • Verleger: Packt Publishing