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

Exploring curiosity-driven reinforcement learning

When we discussed the R2D2 agent, we mentioned that there were only few Atari games left in the benchmark set that the agent could not exceed the human performance in. The remaining challenge for the agent was to solve hard-exploration problems, which have very sparse and/or misleading rewards. Later work came out of Google DeepMind addressed those challenges as well, with agents called Never Give Up (NGU) and Agent57, reaching super-human level performance in all of the 57 games used in the benchmarks. In this section, we are going to discuss these agents and the methods they used for effective exploration.

Let's dive in by describing the concepts of hard-exploration and curiosity-driven learning.

Curiosity-driven learning for hard-exploration problems

Let's consider a simple grid world illustrated in Figure 13.7:

Figure 13.7 – A hard-exploration grid-world problem

Assume the following...