TensorFlow 2 Reinforcement Learning Cookbook

By : Palanisamy P

TensorFlow 2 Reinforcement Learning Cookbook

By: Palanisamy P

Overview of this book

With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch.

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Chapter 1: Developing Building Blocks for Deep Reinforcement Learning Using Tensorflow 2.x

Technical requirements

Building an environment and reward mechanism for training RL agents

Implementing neural network-based RL policies for discrete action spaces and decision-making problems

Implementing neural network-based RL policies for continuous action spaces and continuous-control problems

Working with OpenAI Gym for RL training environments

Building a neural agent

Building a neural evolutionary agent

Free Chapter

Chapter 2: Implementing Value-Based, Policy-Based, and Actor-Critic Deep RL Algorithms

Technical requirements

Building stochastic environments for training RL agents

Building value-based reinforcement learning agent algorithms

Implementing temporal difference learning

Building Monte Carlo prediction and control algorithms for RL

Implementing the SARSA algorithm and an RL agent

Building a Q-learning agent

Implementing policy gradients

Implementing actor-critic RL algorithms

Chapter 3: Implementing Advanced RL Algorithms

Technical requirements

Implementing the Deep Q-Learning algorithm, DQN, and Double-DQN agent

Implementing the Dueling DQN agent

Implementing the Dueling Double DQN algorithm and DDDQN agent

Implementing the Deep Recurrent Q-Learning algorithm and DRQN agent

Implementing the Asynchronous Advantage Actor-Critic algorithm and A3C agent

Implementing the Proximal Policy Optimization algorithm and PPO agent

Implementing the Deep Deterministic Policy Gradient algorithm and DDPG agent

Chapter 4: Reinforcement Learning in the Real World – Building Cryptocurrency Trading Agents

Technical requirements

Building a Bitcoin trading RL platform using real market data

Building an Ethereum trading RL platform using price charts

Building an advanced cryptocurrency trading platform for RL agents

Training a cryptocurrency trading bot using RL

Chapter 5: Reinforcement Learning in the Real World – Building Stock/Share Trading Agents

Technical requirements

Building a stock market trading RL platform using real stock exchange data

Building a stock market trading RL platform using price charts

Building an advanced stock trading RL platform to train agents to mimic professional traders

Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos

Technical requirements

Building learning environments for real-world RL

Building an RL Agent to complete tasks on the web – Call to Action

Building a visual auto-login bot

Training an RL Agent to automate flight booking for your travel

Training an RL Agent to manage your emails

Training an RL Agent to automate your social media account management

Chapter 7: Deploying Deep RL Agents to the Cloud

Technical requirements

Implementing the RL agent’s runtime components

Building RL environment simulators as a service

Training RL agents using a remote simulator service

Testing/evaluating RL agents

Packaging RL agents for deployment – a trading bot

Deploying RL agents to the cloud – a trading Bot-as-a-Service

Chapter 8: Distributed Training for Accelerated Development of Deep RL Agents

Technical requirements

Distributed deep learning models using TensorFlow 2.x – Multi-GPU training

Scaling up and out – Multi-machine, multi-GPU training

Training Deep RL agents at scale – Multi-GPU PPO agent

Building blocks for distributed Deep Reinforcement Learning for accelerated training

Large-scale Deep RL agent training using Ray, Tune, and RLLib

Chapter 9: Deploying Deep RL Agents on Multiple Platforms

Technical requirements

Packaging Deep RL agents for mobile and IoT devices using TensorFlow Lite

Deploying RL agents on mobile devices

Packaging Deep RL agents for the web and Node.js using TensorFlow.js

Deploying a Deep RL agent as a service

Packaging Deep RL agents for cross-platform deployment

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words used in the recipes. Here is an example: "We will start with the implementation of the save method in the Actor class to export the Actor model to TensorFlow's SavedModel format."

A block of code is set as follows:

def save(self, model_dir: str, version: int = 1):
    actor_model_save_dir = os.path.join(model_dir, "actor", str(version), "model.savedmodel")
    self.model.save(actor_model_save_dir, save_format="tf")
    print(f"Actor model saved at:{actor_model_save_dir}")

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

if args.agent != "SAC":
    print(f"Unsupported Agent: {args.agent}. Using SAC Agent")
    args.agent = "SAC"
    # Create an instance of the Soft Actor-Critic Agent
    agent = SAC(env.observation_space.shape, env.action_space)

Any command-line input or output is written as follows:

(tfrl-cookbook)praveen@desktop:~/tensorflow2-reinforcement-learning-cookbook/src/ch7-cloud-deploy-deep-rl-agents$ python 3_training_rl_agents_using_remote_sims.py

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Click on the Open an Existing Project option and you will see a popup asking you to choose the directory on your filesystem. Navigate to the Chapter 9 recipes and choose 9.2_rl_android_app."

Tips or important notes

Appear like this.

TensorFlow 2 Reinforcement Learning Cookbook

By : Palanisamy P

TensorFlow 2 Reinforcement Learning Cookbook

By: Palanisamy P

Overview of this book

Related Content you might be interested in

Current Title:

TensorFlow 2 Reinforcement Learning Cookbook

Hands-On Intelligent Agents with OpenAI Gym

TensorFlow Reinforcement Learning Quick Start Guide

Python Reinforcement Learning Projects

Conventions used