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

REINFORCE with baseline

REINFORCE has the nice property of being unbiased, due to the MC return, which provides the true return of a full trajectory. However, the unbiased estimate is to the detriment of the variance, which increases with the length of the trajectory. Why? This effect is due to the stochasticity of the policy. By executing a full trajectory, you would know its true reward. However, the value that is assigned to each state-action pair may not be correct, since the policy is stochastic, and executing it another time may lead to a new state, and consequently, a different reward. Moreover, you can see that the higher the number of actions in a trajectory, the more stochasticity you will have introduced into the system, therefore, ending up with higher variance.

Luckily, it is possible to introduce a baseline, , in the estimation of the return, therefore decreasing...