In the previous chapters, we looked at reinforcement learning algorithms, ranging from value-based to policy-based methods and from model-free to model-based methods. In this chapter, we'll provide another solution for solving sequential tasks, that is, with a class of black-box algorithms evolutionary algorithms (EA). EAs are driven by evolutionary mechanisms and are sometimes preferred to reinforcement learning (RL) as they don't require backpropagation. They also offer other complementary benefits to RL. We'll start this chapter by giving you a brief recap of RL algorithms so that you'll better understand how EA fits into these sets of problems. Then, you'll learn about the basic building blocks of EA and how those algorithms work. We'll also take advantage of this introduction and look at one of...
Reinforcement Learning Algorithms with Python
By :
Reinforcement Learning Algorithms with Python
By:
Overview of this book
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.
Related Content you might be interested in
Current Title:
Reinforcement Learning Algorithms with Python
Table of Contents (19 chapters)
Preface
The Landscape of Reinforcement Learning
Implementing RL Cycle and OpenAI Gym
Solving Problems with Dynamic Programming
Section 2: Model-Free RL Algorithms
Q-Learning and SARSA Applications
Deep Q-Network
Learning Stochastic and PG Optimization
TRPO and PPO Implementation
DDPG and TD3 Applications
Section 3: Beyond Model-Free Algorithms and Improvements
Model-Based RL
Imitation Learning with the DAgger Algorithm
Understanding Black-Box Optimization Algorithms
Developing the ESBAS Algorithm
Practical Implementation for Resolving RL Challenges
Assessments
Other Books You May Enjoy
Customer Reviews