Reinforcement Learning Algorithms with Python: Learn,understand,and develop smart algorithms for addressing AI challenges
Authors: Andrea Lonza
ISBN-10: 1789131111
ISBN-13: 9781789131116
Released: 2019-10-18
Print Length 页数: 366 pages
Book Description
Develop self-learning algorithms and agents using TensorFlow and other Python tools,frameworks,and libraries
Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents.
Starting with an introduction to the tools,libraries,and setup needed to work in the RL environment,this book covers the building blocks of RL and delves into value-based methods,such as the application of Q-learning and SARSA algorithms. You’ll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore,you’ll study the policy gradient methods,TRPO,and PPO,to improve performance and stability,before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to drive. You’ll discover evolutionary strategies and black-box optimization techniques,and see how they can improve RL algorithms. Finally,you’ll get to grips with exploration approaches,such as UCB and UCB1,and develop a meta-algorithm called ESBAS.
By the end of the book,you’ll have worked with key RL algorithms to overcome challenges in real-world applications,and be part of the RL research community.
What you will learn
Develop an agent to play CartPole using the OpenAI Gym interface
Discover the model-based reinforcement learning paradigm
Solve the Frozen Lake problem with dynamic programming
Explore Q-learning and SARSA with a view to playing a taxi game
Apply Deep Q-Networks (DQNs) to Atari games using Gym
Study policy gradient algorithms,including Actor-Critic and REINFORCE
Understand and apply PPO and TRPO in continuous locomotion environments
Get to grips with evolution strategies for solving the lunar lander problem
contents
1 The Landscape of Reinforcement Learning
2 Implementing RL Cycle and OpenAI Gym
3 Solving Problems with Dynamic Programming
4 Q-Learning and SARSA Applications
5 Deep Q-Network
6 Learning Stochastic and PG Optimization
7 TRPO and PPO Implementation
8 DDPG and TD3 Applications
9 Model-Based RL
10 Imitation Learning with the DAgger Algorithm
11 Understanding Black-Box Optimization Algorithms
12 Developing the ESBAS Algorithm
13 Practical Implementation for Resolving RL Challenges
