Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Reinforcement Learning with TensorFlow
  • Toc
  • feedback
Reinforcement Learning with TensorFlow

Reinforcement Learning with TensorFlow

By : Dutta
2.2 (5)
close
Reinforcement Learning with TensorFlow

Reinforcement Learning with TensorFlow

2.2 (5)
By: Dutta

Overview of this book

Reinforcement learning (RL) allows you to develop smart, quick and self-learning systems in your business surroundings. It's an effective method for training learning agents and solving a variety of problems in Artificial Intelligence - from games, self-driving cars and robots, to enterprise applications such as data center energy saving (cooling data centers) and smart warehousing solutions. The book covers major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You'll also be introduced to the concept of reinforcement learning, its advantages and the reasons why it's gaining so much popularity. You'll explore MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, and temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have gained a firm understanding of what reinforcement learning is and understand how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.
Table of Contents (17 chapters)
close
close
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Deep Learning – Architectures and Frameworks
Deep Learning – Architectures and Frameworks
Deep learning
Reinforcement learning
Introduction to TensorFlow and OpenAI Gym
The pioneers and breakthroughs in reinforcement learning
Summary
2
Training Reinforcement Learning Agents Using OpenAI Gym
Training Reinforcement Learning Agents Using OpenAI Gym
The OpenAI Gym
Programming an agent using an OpenAI Gym environment
Summary
3
Markov Decision Process
Markov Decision Process
Markov decision processes
Partially observable Markov decision processes
Training the FrozenLake-v0 environment using MDP
Summary
4
Policy Gradients
Policy Gradients
The policy optimization method
Why policy optimization methods?
Policy objective functions
Temporal difference rule
Policy gradients
Agent learning pong using policy gradients
Summary
5
Q-Learning and Deep Q-Networks
chevron up
Q-Learning and Deep Q-Networks
Why reinforcement learning?
Model based learning and model free learning
Q-learning
Deep Q-networks
The Monte Carlo tree search algorithm
The SARSA algorithm
Summary
6
Asynchronous Methods
Asynchronous Methods
Why asynchronous methods?
Asynchronous one-step Q-learning
Asynchronous one-step SARSA
Asynchronous n-step Q-learning
Asynchronous advantage actor critic
A3C for Pong-v0 in OpenAI gym
Summary
7
Robo Everything – Real Strategy Gaming
Robo Everything – Real Strategy Gaming
Real-time strategy games
Reinforcement learning and other approaches
Reinforcement learning in RTS gaming
Summary
8
AlphaGo – Reinforcement Learning at Its Best
AlphaGo – Reinforcement Learning at Its Best
What is Go?
AlphaGo – mastering Go
AlphaGo Zero
Summary
9
Reinforcement Learning in Autonomous Driving
Reinforcement Learning in Autonomous Driving
Machine learning for autonomous driving
Reinforcement learning for autonomous driving
Proposed frameworks for autonomous driving
DeepTraffic – MIT simulator for autonomous driving 
Summary
10
Financial Portfolio Management
Financial Portfolio Management
Introduction
Problem definition
Data preparation
Reinforcement learning
Further improvements
Summary
11
Reinforcement Learning in Robotics
Reinforcement Learning in Robotics
Reinforcement learning in robotics
Challenges in robot reinforcement learning
Open questions and practical challenges
Key takeaways
Summary
12
Deep Reinforcement Learning in Ad Tech
Deep Reinforcement Learning in Ad Tech
Computational advertising challenges and bidding strategies
Real-time bidding by reinforcement learning in display advertising
Summary
13
Reinforcement Learning in Image Processing
Reinforcement Learning in Image Processing
Hierarchical object detection with deep reinforcement learning
Summary
14
Deep Reinforcement Learning in NLP
Deep Reinforcement Learning in NLP
Text summarization
Text question answering
Summary
15
Further topics in Reinforcement Learning
Further topics in Reinforcement Learning
Continuous action space algorithms
Scoring mechanism in sequential models in NLP
Summary
16
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Deep Q-networks

If we recall Chapter 2, Training Reinforcement Learning Agents Using OpenAI Gym, where we tried to implement a basic Q-network, we studied that for a real-world problem, Q-learning using a Q-table is not a feasible solution owing to continuous state and action spaces. Moreover, a Q-table is environment-specific and not generalized. Therefore, we need a model which can map the state information provided as input to Q-values of the possible set of actions. This is where a neural network comes to play the role of a function approximator, which can take state information input in the form of a vector, and learn to map them to Q-values for all possible actions.

Let's discuss the issues with Q-learning in a gaming environment and evolution of deep Q-networks. Consider applying Q-learning to a gaming environment, the state would be defined by the location of...

End of Section 5
Next Section
bookmark search playlist download
font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete