With significant enhancement in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been completely revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow and the OpenAI Gym toolkit.

In addition to exploring RL basics and foundational concepts such as the Bellman equation, Markov decision processes, and dynamic programming, this second edition dives deep into the full spectrum of value-based, policy-based, and actor- critic RL methods with detailed math. 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 implement popular RL algorithms effortlessly. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research.

Download the detailed and complete table of contents from here.

• 1. Fundamentals of Reinforcement Learning
• 2. A Guide to the Gym Toolkit
• 3. 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. Getting to Know 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

1. Understand core RL concepts including the methodologies, math, and code
2. Train an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI Gym
3. Train an agent to play Ms Pac-Man using a Deep Q Network
4. Learn policy-based, value-based, and actor-critic methods
5. Master the math behind DDPG, TD3, TRPO, PPO, and many others
6. Explore new avenues such as the distributional RL, meta RL, and inverse RL
7. Use Stable Baselines to train an agent to walk and play Atari games

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

https://packt.link/free-ebook/9781839210686