Book Image

Mastering Reinforcement Learning with Python

By : Enes Bilgin
Book Image

Mastering Reinforcement Learning with Python

By: Enes Bilgin

Overview of this book

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.
Table of Contents (24 chapters)
Section 1: Reinforcement Learning Foundations
Section 2: Deep Reinforcement Learning
Section 3: Advanced Topics in RL
Section 4: Applications of RL

Providing ancillary service to power grid

In this section, we describe how reinforcement learning can help with integrating clean energy resources into power grid by managing smart appliances in home and office buildings.

Power grid operations and ancillary services

Transmission and distribution of electricity power from generators to consumers is a massive operation that requires continuous monitoring and control of the system. In particular, the generation and consumption should be nearly equal in a region to keep the electric current at the standard frequency (60 Hz in the United States) to prevent blackouts and damages. This is a challenging undertaking for various reasons:

  • Power supply is planned ahead in energy markets with the generators in the region to match the demand.
  • Despite the planning, future power supply is uncertain, especially when obtained from renewable resources. The amount of wind and solar may be less or more than expected, causing under or...