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

Meta-reinforcement learning with recurrent policies

In this section, we cover one of the more intuitive approaches in meta-reinforcement learning that uses recurrent neural networks to keep a memory, also known as the RL­2 algorithm. Let's start with an example to motivate this approach.

Grid world example

Consider a grid world where the agent's task is to reach a goal state G from a start state S. These states are randomly placed for different tasks, so the agent has to learn exploring the world to discover where the goals state is, which then is given a big reward. When the same task is repeated, the agent is expected to quickly reach the goal state, which is, adapt to the environment, since there is a penalty incurred for each time step. This is described in Figure 12.1.

Figure 12.1 – Grid world example for meta-RL. (a) A task, (b) agent's exploration of the task, (c) agent's exploitation of what it learned.

In order...