Book Image

Reinforcement Learning Algorithms with Python

By : Andrea Lonza
Book Image

Reinforcement Learning Algorithms with Python

By: Andrea Lonza

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.
Table of Contents (19 chapters)
Free Chapter
1
Section 1: Algorithms and Environments
5
Section 2: Model-Free RL Algorithms
11
Section 3: Beyond Model-Free Algorithms and Improvements
17
Assessments

SARSA

So far, we have presented TD learning as a general way to estimate a value function for a given policy. In practice, TD cannot be used as it is because it lacks the primary component to actually improve the policy. SARSA and Q-learning are two one-step, tabular TD algorithms that both estimate the value functions and optimize the policy, and that can actually be used in a great variety of RL problems. In this section, we will use SARSA to learn an optimal policy for a given MDP. Then, we'll introduce Q-learning.

A concern with TD learning is that it estimates the value of a state. Think about that. In a given state, how can you choose the action with the highest next state value? Earlier, we said that you should pick the action that will move the agent to the state with the highest value. However, without a model of the environment that provides a list of the possible...