Hands-On Reinforcement Learning with Pythonepub,mobi,azw3格式电子书下载，作者：Sudharsan Ravichandiran-大蓝莓

Learn

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

About

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.

By the end of the 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.

Features

Your entry point into the world of artificial intelligence using the power of Python

An example-rich guide to master various RL and DRL algorithms

Explore various state-of-the-art architectures along with math

Hands-On Reinforcement Learning with Python

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