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Deep Reinforcement Learning Hands-On
Deep Reinforcement Learning Hands-On

Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more , Second Edition

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Deep Reinforcement Learning Hands-On

OpenAI Gym

After talking so much about the theoretical concepts of reinforcement learning (RL) in Chapter 1, What Is Reinforcement Learning?, let's start doing something practical! In this chapter, you will learn the basics of OpenAI Gym, a library used to provide a uniform API for an RL agent and lots of RL environments. This removes the need to write boilerplate code.

You will also write your first randomly behaving agent and become more familiar with the basic concepts of RL that we have covered so far. By the end of the chapter, you will have an understanding of:

  • The high-level requirements that need to be implemented to plug the agent into the RL framework
  • A basic, pure-Python implementation of the random RL agent
  • OpenAI Gym

The anatomy of the agent

As you learned in the previous chapter, there are several entities in RL's view of the world:

  • The agent: A thing, or person, that takes an active role. In practice, the agent is some piece of code that implements some policy. Basically, this policy decides what action is needed at every time step, given our observations.
  • The environment: Some model of the world that is external to the agent and has the responsibility of providing observations and giving rewards. The environment changes its state based on the agent's actions.

Let's explore how both can be implemented in Python for a simple situation. We will define an environment that will give the agent random rewards for a limited number of steps, regardless of the agent's actions. This scenario is not very useful, but it will allow us to focus on specific methods in both the environment and agent classes. Let's start with the environment:

class Environment:
...

Hardware and software requirements

The examples in this book were implemented and tested using Python version 3.7. I assume that you're already familiar with the language and common concepts such as virtual environments, so I won't cover in detail how to install the package and how to do this in an isolated way. The examples will use the previously mentioned Python type annotations, which will allow us to provide type signatures for functions and class methods.

The external libraries that we will use in this book are open source software, and they include the following:

  • NumPy: This is a library for scientific computing, and implementing matrix operations and common functions.
  • OpenCV Python bindings: This is a computer vision library and provides many functions for image processing.
  • Gym: This is an RL framework that has various environments that can be communicated with in a unified way.
  • PyTorch: This is a flexible and expressive deep learning (DL) library...

The OpenAI Gym API

The Python library called Gym was developed and has been maintained by OpenAI (www.openai.com). The main goal of Gym is to provide a rich collection of environments for RL experiments using a unified interface. So, it is not surprising that the central class in the library is an environment, which is called Env. Instances of this class expose several methods and fields that provide the required information about its capabilities. At a high level, every environment provides these pieces of information and functionality:

  • A set of actions that is allowed to be executed in the environment. Gym supports both discrete and continuous actions, as well as their combination
  • The shape and boundaries of the observations that the environment provides the agent with
  • A method called step to execute an action, which returns the current observation, the reward, and the indication that the episode is over
  • A method called reset, which returns the environment to...

The random CartPole agent

Although the environment is much more complex than our first example in The anatomy of the agent section, the code of the agent is much shorter. This is the power of reusability, abstractions, and third-party libraries!

So, here is the code (you can find it in Chapter02/02_cartpole_random.py).

import gym
if __name__ == "__main__":
    env = gym.make("CartPole-v0")
    total_reward = 0.0
    total_steps = 0
    obs = env.reset()

Here, we created the environment and initialized the counter of steps and the reward accumulator. On the last line, we reset the environment to obtain the first observation (which we will not use, as our agent is stochastic).

    while True:
        action = env.action_space.sample()
        obs, reward, done, _ = env.step(action)
        total_reward += reward
        total_steps += 1
        if done:
            break
    print("Episode done in %d steps, total reward %.2f&quot...

Extra Gym functionality – wrappers and monitors

What we have discussed so far covers two-thirds of the Gym core API and the essential functions required to start writing agents. The rest of the API you can live without, but it will make your life easier and your code cleaner. So, let's briefly cover the rest of the API.

Wrappers

Very frequently, you will want to extend the environment's functionality in some generic way. For example, imagine an environment gives you some observations, but you want to accumulate them in some buffer and provide to the agent the N last observations. This is a common scenario for dynamic computer games, when one single frame is just not enough to get the full information about the game state. Another example is when you want to be able to crop or preprocess an image's pixels to make it more convenient for the agent to digest, or if you want to normalize reward scores somehow. There are many such situations that have the same...

Summary

You have started to learn about the practical side of RL! In this chapter, we installed OpenAI Gym, with its tons of environments to play with. We studied its basic API and created a randomly behaving agent.

You also learned how to extend the functionality of existing environments in a modular way and became familiar with a way to record our agent's activity using the Monitor class. This will be heavily used in the upcoming chapters.

In the next chapter, we will do a quick DL recap using PyTorch, which is a favorite library among DL researchers. Stay tuned.

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Key benefits

  • Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters
  • Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods
  • Apply RL methods to cheap hardware robotics platforms

Description

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

Who is this book for?

Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL

What you will learn

  • Understand the deep learning context of RL and implement complex deep learning models
  • Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others
  • Build a practical hardware robot trained with RL methods for less than $100
  • Discover Microsoft s TextWorld environment, which is an interactive fiction games platform
  • Use discrete optimization in RL to solve a Rubik s Cube
  • Teach your agent to play Connect 4 using AlphaGo Zero
  • Explore the very latest deep RL research on topics including AI chatbots
  • Discover advanced exploration techniques, including noisy networks and network distillation techniques

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Table of Contents

27 Chapters
What Is Reinforcement Learning? Chevron down icon Chevron up icon
OpenAI Gym Chevron down icon Chevron up icon
Deep Learning with PyTorch Chevron down icon Chevron up icon
The Cross-Entropy Method Chevron down icon Chevron up icon
Tabular Learning and the Bellman Equation Chevron down icon Chevron up icon
Deep Q-Networks Chevron down icon Chevron up icon
Higher-Level RL Libraries Chevron down icon Chevron up icon
DQN Extensions Chevron down icon Chevron up icon
Ways to Speed up RL Chevron down icon Chevron up icon
Stocks Trading Using RL Chevron down icon Chevron up icon
Policy Gradients – an Alternative Chevron down icon Chevron up icon
The Actor-Critic Method Chevron down icon Chevron up icon
Asynchronous Advantage Actor-Critic Chevron down icon Chevron up icon
Training Chatbots with RL Chevron down icon Chevron up icon
The TextWorld Environment Chevron down icon Chevron up icon
Web Navigation Chevron down icon Chevron up icon
Continuous Action Space Chevron down icon Chevron up icon
RL in Robotics Chevron down icon Chevron up icon
Trust Regions – PPO, TRPO, ACKTR, and SAC Chevron down icon Chevron up icon
Black-Box Optimization in RL Chevron down icon Chevron up icon
Advanced Exploration Chevron down icon Chevron up icon
Beyond Model-Free – Imagination Chevron down icon Chevron up icon
AlphaGo Zero Chevron down icon Chevron up icon
RL in Discrete Optimization Chevron down icon Chevron up icon
Multi-agent RL Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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