Deep Reinforcement Learning Hands-On: Apply modern RL methods,with deep Q-networks,value iteration,policy gradients,TRPO,AlphaGo Zero and more
by: Maxim Lapan
ISBN-10: 1788834240
ISBN-13: 9781788834247
Publication Date 出版日期: 2018-06-21
Print Length 页数: 546
Book Description
By finelybook
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. Google’s 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.
Contents
1: WHAT IS REINFORCEMENT LEARNING?
2: OPENAI GYM
3: DEEP LEARNING WITH PYTORCH
4: THE CROSS-ENTROPY METHOD
5: TABULAR LEARNING AND THE BELLMAN EQUATION
6: DEEP Q-NETWORKS
7: DQN EXTENSIONS
8: STOCKS TRADING USING RL
9: POLICY GRADIENTS – AN ALTERNATIVE
10: THE ACTOR-CRITIC METHOD
11: ASYNCHRONOUS ADVANTAGE ACTOR-CRITIC
12: CHATBOTS TRAINING WITH RL
13: WEB NAVIGATION
14: CONTINUOUS ACTION SPACE
15: TRUST REGIONS – TRPO,PPO,AND ACKTR
16: BLACK-BOX OPTIMIZATION IN RL
17: BEYOND MODEL-FREE – IMAGINATION
18: ALPHAGO ZERO
What You Will Learn
Understand the DL context of RL and implement complex DL models
Learn the foundation of RL: Markov decision processes
Evaluate RL methods including Cross-entropy,DQN,Actor-Critic,TRPO,PPO,DDPG,D4PG and others
Discover how to deal with discrete and continuous action spaces in various environments
Defeat Atari arcade games using the value iteration method
Create your own OpenAI Gym environment to train a stock trading agent
Teach your agent to play Connect4 using AlphaGo Zero
Explore the very latest deep RL research on topics including AI-driven chatbots
Authors
Maxim Lapan
Maxim Lapan is a deep learning enthusiast and independent researcher. His background and 15 years’ work expertise as a software developer and a systems architect lays from low-level Linux kernel driver development to performance optimization and design of distributed applications working on thousands of servers. With vast work experiences in big data,Machine Learning,and large parallel distributed HPC and nonHPC systems,he has a talent to explain a gist of complicated things in simple words and vivid examples. His current areas of interest lie in practical applications of Deep Learning,such as Deep Natural Language Processing and Deep Reinforcement Learning. Maxim lives in Moscow,Russian Federation,with his family,and he works for an Israeli start-up as a Senior NLP developer.
