num_pages: 548
description: This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Recent developments in reinforcement learning (RL) combined with deep learning (DL) have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Googles use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on grid world environments teach your agent to buy and trade stocks and find out how natural language models are driving the boom in chatbots. Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.
language: English
title: Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
publisher: Packt Publishing