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)
1
Section 1: Reinforcement Learning Foundations
7
Section 2: Deep Reinforcement Learning
12
Section 3: Advanced Topics in RL
17
Section 4: Applications of RL

Temporal-difference learning

The first class of methods to solve MDP we covered in this chapter was DP, which

  • Requires to completely know the environment dynamics to be able find the optimal solution.
  • Allow us to progress toward the solution with one-step updates of the value functions.

We then covered the MC methods, which

  • Only require the ability to sample from the environment, therefore learn from experience, as opposed to knowing the environment dynamics - a huge advantage over DP,
  • But need to wait for a complete episode trajectory to update a policy.

Temporal-difference (TD) methods are, in some sense, the best of both worlds: They learn from experience, and they can update the policy after each step by bootstrapping. This comparison of TD to DP and MC is illustrated in Table 5.2.

Table 5.2 – Comparison of DP, MC, and TD learning methods

As a result, TD methods are central in RL, and you will encounter them...