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The Reinforcement Learning Workshop

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
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The Reinforcement Learning Workshop

The Reinforcement Learning Workshop

4.7 (7)
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)
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Preface
Free Chapter
2
2. Markov Decision Processes and Bellman Equations

Combining a DQN with a CNN

Humans play video games using their sight. They look at the screen, analyze the situation, and decide what the best action to be performed is. In video games, there can be a lot of things happening on the screen, so being able to see all these patterns can give a significant advantage in playing the game. Combining a DQN with a CNN can help a reinforcement learning agent to learn the right action to take given a particular situation.

Instead of just using fully connected layers, a DQN model can be extended with convolutional layers as inputs. The model will then be able to analyze the input image, find the relevant patterns, and feed them to the fully connected layers responsible for predicting the Q-values, as shown in the following:

Figure 10.6: Difference between a normal DQN and a DQN combined with convolutional layers

Adding convolutional layers helps the agent to better understand the environment. The DQN agent that we will...

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