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Home > Data > Reinforcement Learning > TensorFlow 2 Reinforcement Learning Cookbook
TensorFlow 2 Reinforcement Learning Cookbook
TensorFlow 2 Reinforcement Learning Cookbook

TensorFlow 2 Reinforcement Learning Cookbook: Over 50 recipes to help you build, train, and deploy learning agents for real-world applications

By Palanisamy P
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TensorFlow 2 Reinforcement Learning Cookbook

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

This chapter provides a practical approach to building value-based, policy-based, and actor-critic algorithm-based reinforcement learning (RL) agents. It includes recipes for implementing value iteration-based learning agents and breaks down the implementation details of several foundational algorithms in RL into simple steps. The policy gradient-based agent and the actor-critic agent make use of the latest major version of TensorFlow 2.x to define the neural network policies.

The following recipes will be covered in this chapter:

  • Building stochastic environments for training RL agents
  • Building value-based (RL) 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...

Technical requirements

The code in this book has been tested extensively on Ubuntu 18.04 and Ubuntu 20.04, and should work with later versions of Ubuntu if Python 3.6+ is available. With Python 3.6 installed, along with the necessary Python packages listed at the beginning of each recipe, the code should run fine on Windows and Mac OS X too. It is advised that you create and use a Python virtual environment named tf2rl-cookbook to install the packages and run the code in this book. Installing Miniconda or Anaconda for Python virtual environment management is recommended.

The complete code for each recipe in each chapter is available here: https://github.com/PacktPublishing/Tensorflow-2-Reinforcement-Learning-Cookbook.

Building stochastic environments for training RL agents

To train RL agents for the real world, we need learning environments that are stochastic, since real-world problems are stochastic in nature. This recipe will walk you through the steps for building a Maze learning environment to train RL agents. The Maze is a simple, stochastic environment where the world is represented as a grid. Each location on the grid can be referred to as a cell. The goal of an agent in this environment is to find its way to the goal state. Consider the maze shown in the following diagram, where the black cells represent walls:

Figure 2.1 – The Maze environment

The agent's location is initialized to be at the top-left cell in the Maze. The agent needs to find its way around the grid to reach the goal located at the top-right cell in the Maze, collecting a maximum number of coins along the way while avoiding walls. The location of the goal, coins, walls, and the agent...

Building value-based reinforcement learning agent algorithms

Value-based reinforcement learning works by learning the state-value function or the action-value function in a given environment. This recipe will show you how to create and update the value function for the Maze environment to obtain an optimal policy. Learning value functions, especially in model-free RL problems where a model of the environment is not available, can prove to be quite effective, especially for RL problems with low-dimensional state space.

Upon completing this recipe, you will have an algorithm that can generate the following optimal action sequence based on value functions:

Figure 2.3 – Optimal action sequence generated by a value-based RL algorithm with state values represented through a jet color map

Let's get started.

Getting ready

To complete this recipe, you will need to activate the tf2rl-cookbook Python/conda virtual environment and run pip install numpy...

Implementing temporal difference learning

This recipe will walk you through how to implement the temporal difference (TD) learning algorithm. TD algorithms allow us to incrementally learn from incomplete episodes of agent experiences, which means they can be used for problems that require online learning capabilities. TD algorithms are useful in model-free RL settings as they do not depend on a model of the MDP transitions or rewards. To visually understand the learning progression of the TD algorithm, this recipe will also show you how to implement the GridworldV2 learning environment, which looks as follows when rendered:

Figure 2.6 – The GridworldV2 learning environment 2D rendering with state values and grid cell coordinates

Getting ready

To complete this recipe, you will need to activate the tf2rl-cookbook Python/conda virtual environment and run pip install numpy gym. If the following import statements run without issues, you are ready to get...

Building Monte Carlo prediction and control algorithms for RL

This recipe provides the ingredients for building a Monte Carlo prediction and control algorithm so that you can build your RL agents. Similar to the temporal difference learning algorithm, Monte Carlo learning methods can be used to learn both the state and the action value functions. Monte Carlo methods have zero bias since they learn from complete episodes with real experience, without approximate predictions. These methods are suitable for applications that require good convergence properties. The following diagram illustrates the value that's learned by the Monte Carlo method for the GridworldV2 environment:

Figure 2.10 – Monte Carlo prediction of state values (left) and state-action values (right)

Getting ready

To complete this recipe, you will need to activate the tf2rl-cookbook Python/conda virtual environment and run pip install -r requirements.txt. If the following import...

Implementing the SARSA algorithm and an RL agent

This recipe will show you how to implement the State-Action-Reward-State-Action (SARSA) algorithm, as well as how to develop and train an agent using the SARSA algorithm so that it can act in a reinforcement learning environment. The SARSA algorithm can be applied to model-free control problems and allows us to optimize the value function of an unknown MDP.

Upon completing this recipe, you will have a working RL agent that, when acting in the GridworldV2 environment, will generate the following state-action value function using the SARSA algorithm:

Figure 2.15 – Rendering of the GridworldV2 environment – each triangle represents the action value of taking that directional action in that grid state

Getting ready

To complete this recipe, you will need to activate the tf2rl-cookbook Python/conda virtual environment and run pip install -r requirements.txt. If the following import statements run...

Building a Q-learning agent

This recipe will show you how to build a Q-learning agent. Q-learning can be applied to model-free RL problems. It supports off-policy learning and therefore provides a practical solution to problems where available experiences were/are collected using some other policy or by some other agent (even humans).

Upon completing this recipe, you will have a working RL agent that, when acting in the GridworldV2 environment, will generate the following state-action value function using the SARSA algorithm:

Figure 2.18 – State-action values obtained using the Q-learning algorithm

Getting ready

To complete this recipe, you will need to activate the tf2rl-cookbook Python/conda virtual environment and run pip install -r requirements.txt. If the following import statements run without issues, you are ready to get started:

import numpy as np
import random

Now, let's begin.

How to do it…

Let's implement...

Implementing policy gradients

Policy gradient algorithms are fundamental to reinforcement learning and serve as the basis for several advanced RL algorithms. These algorithms directly optimize for the best policy, which can lead to faster learning compared to value-based algorithms. Policy gradient algorithms are effective for problems/applications with high-dimensional or continuous action spaces. This recipe will show you how to implement policy gradient algorithms using TensorFlow 2.0. Upon completing this recipe, you will be able to train an RL agent in any compatible OpenAI Gym environment.

Getting ready

To complete this recipe, you will need to activate the tf2rl-cookbook Python/conda virtual environment and run pip install -r requirements.txt. If the following import statements run without issues, you are ready to get started:

import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow import keras
from tensorflow.keras import layers
import numpy as...

Implementing actor-critic RL algorithms

Actor-critic algorithms allow us to combine value-based and policy-based reinforcement learning – an all-in-one agent. While policy gradient methods directly search and optimize the policy in the policy space, leading to smoother learning curves and improvement guarantees, they tend to get stuck at the local maxima (for a long-term reward optimization objective). Value-based methods do not get stuck at local optimum values, but they lack convergence guarantees, and algorithms such as Q-learning tend to have high variance and are not very sample-efficient. Actor-critic methods combine the good qualities of both value-based and policy gradient-based algorithms. Actor-critic methods are also more sample-efficient. This recipe will make it easy for you to implement an actor-critic-based RL agent using TensorFlow 2.x. Upon completing this recipe, you will be able to train the actor-critic agent in any OpenAI Gym-compatible reinforcement learning...

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Key benefits

  • Develop and deploy deep reinforcement learning-based solutions to production pipelines, products, and services
  • Explore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic method
  • Customize and build RL-based applications for performing real-world tasks

Description

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.

What you will learn

Build deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras API Implement state-of-the-art deep reinforcement learning algorithms using minimal code Build, train, and package deep RL agents for cryptocurrency and stock trading Deploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud services Speed up agent development using distributed DNN model training Explore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)
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Product Details

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Publication date : Jan 15, 2021
Length 472 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781838982546
Vendor :
Google
Category :
Data
Languages :
Python (Expert)
Concepts :
Reinforcement Learning
Tools :
TensorFlow (Intermediate)

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Estimated delivery fee Deliver to Singapore

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Product Details

Publication date : Jan 15, 2021
Length 472 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781838982546
Vendor :
Google
Category :
Data
Languages :
Python (Expert)
Concepts :
Reinforcement Learning
Tools :
TensorFlow (Intermediate)

Table of Contents

11 Chapters
Preface Chevron down icon Chevron up icon
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
1. Chapter 1: Developing Building Blocks for Deep Reinforcement Learning Using Tensorflow 2.x Chevron down icon Chevron up icon
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
2. Chapter 2: Implementing Value-Based, Policy-Based, and Actor-Critic Deep RL Algorithms Chevron down icon Chevron up icon
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
3. Chapter 3: Implementing Advanced RL Algorithms Chevron down icon Chevron up icon
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
4. Chapter 4: Reinforcement Learning in the Real World – Building Cryptocurrency Trading Agents Chevron down icon Chevron up icon
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
5. Chapter 5: Reinforcement Learning in the Real World – Building Stock/Share Trading Agents Chevron down icon Chevron up icon
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
6. Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos Chevron down icon Chevron up icon
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
7. Chapter 7: Deploying Deep RL Agents to the Cloud Chevron down icon Chevron up icon
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
8. Chapter 8: Distributed Training for Accelerated Development of Deep RL Agents Chevron down icon Chevron up icon
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
9. Chapter 9: Deploying Deep RL Agents on Multiple Platforms Chevron down icon Chevron up icon
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
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Profile icon Palanisamy P
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Praveen Palanisamy works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R&D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.
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Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela