Book Image

The Reinforcement Learning Workshop

By : Alessandro Palmas, Emanuele Ghelfi, Dr. Alexandra Galina Petre, Mayur Kulkarni, Anand N.S., Quan Nguyen, Aritra Sen, Anthony So, Saikat Basak
Book Image

The Reinforcement Learning Workshop

By: Alessandro Palmas, Emanuele Ghelfi, Dr. Alexandra Galina Petre, Mayur Kulkarni, Anand N.S., Quan Nguyen, Aritra Sen, Anthony So, Saikat Basak

Overview of this book

Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, youÔÇÖll be guided through different RL environments and frameworks. YouÔÇÖll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once youÔÇÖve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, youÔÇÖll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, youÔÇÖll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, youÔÇÖll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.
Table of Contents (14 chapters)
Preface
Free Chapter
2
2. Markov Decision Processes and Bellman Equations

Building a DRQN

A DQN can benefit greatly from RNN models facilitating the processing of sequential images. Such an architecture is known as Deep Recurrent Q Network (DRQN). Combining a GRU or LSTM model with a CNN model will allow the reinforcement learning agent to understand the movement of the ball. To do so, we just need to add an LSTM (or GRU) layer between the convolutional and fully connected layers, as shown in the following figure:

Figure 10.9: DRQN architecture

To feed the RNN model with a sequence of images, we need to stack several images together. For the Breakout game, after initializing the environment, we will need to take the first image and duplicate it several times in order to have the first initial sequence of images. Having done this, after each action, we can append the latest image to the sequence and remove the oldest one in order to maintain the exact same size of sequence (for instance, a sequence of a maximum of four images).

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