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

RLlib: Production-grade deep reinforcement learning

As we mentioned at the beginning, one of the motivations of Ray's creators is to build an easy-to-use distributed computing framework that can handle complex and heterogenous applications such as deep reinforcement learning. With that, they also created a widely-used deep RL library based on Ray. Training a model similar to ours is very simple using RLlib. The main steps are:

  • Import the default training configs for Ape-X DQN as well as the trainer,
  • Customize the training configs,
  • Train the trainer.

That's it! The code necessary for that is very simple. All you need is the following:


import pprint
from ray import tune
from ray.rllib.agents.dqn.apex import APEX_DEFAULT_CONFIG
from ray.rllib.agents.dqn.apex import ApexTrainer
if __name__ == '__main__':
    config = APEX_DEFAULT_CONFIG.copy()
    pp = pprint.PrettyPrinter...