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

Using memory to overcome partial observability

A memory is nothing but a way of processing a sequence of observations as the input to the agent policy. If you worked with other types of sequence data with neural networks, such as in time series prediction or natural language processing (NLP), you can adopt similar approaches to use observation memory as the input your RL model.

Let's go into more details of how this can be done.

Stacking observations

A simple way of passing an observation sequence to the model is to stitch them together and treat this stack as a single observation. Denoting the observation at time as , we can form a new observation to be passed to the model as follows:

where is the length of the memory. Of course, for , we need to somehow initialize the earlier parts of the memory, such as using vectors of zeros that are the same dimension as .

In fact, simply stacking observations is how the original DQN work handled...