Hands-On Reinforcement Learning with Python

eBook Details:

• Paperback: 318 pages
• Publisher: WOW! eBook (June 28, 2018)
• Language: English
• ISBN-10: 1788836529
• ISBN-13: 978-1788836524

eBook Description:

Hands-On Reinforcement Learning with Python: A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.

The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.

• Understand the basics of reinforcement learning methods, algorithms, and elements
• Train an agent to walk using OpenAI Gym and Tensorflow
• Understand the Markov Decision Process, Bellman’s optimality, and TD learning
• Solve multi-armed-bandit problems using various algorithms
• Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
• Build intelligent agents using the DRQN algorithm to play the Doom game
• Teach agents to play the Lunar Lander game using DDPG
• Train an agent to win a car racing game using dueling DQN

By the end of the Hands-On Reinforcement Learning with Python book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.