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

Controlling traffic lights to optimize vehicle flow

One of the key challenges for cities is to optimize traffic flows on road networks. There are numerous benefits in reducing traffic congestions, including but not limited to:

  • Reducing the time and energy wasted in traffic
  • Saving on gas and resulting exhaust emissions
  • Increasing vehicle and road lifetime
  • Decreasing number of accidents

There has been already a lot of research going in this area; but recently, RL has emerged as a competitive alternative to traditional control approaches. So, in this section, we optimize the traffic flow at a road network by controlling the traffic light behavior using multi-agent reinforcement learning. To this end, we use the Flow framework, which is an open-source library for RL and control experiments on realistic traffic microsimulations.

Introducing Flow

Transportation research significantly relies on simulation software, such as SUMO and Aimsun, for topics such...