Book Image

TensorFlow Reinforcement Learning Quick Start Guide

By : Kaushik Balakrishnan
Book Image

TensorFlow Reinforcement Learning Quick Start Guide

By: Kaushik Balakrishnan

Overview of this book

Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems.

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Table of Contents (11 chapters)
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
Up and Running with Reinforcement Learning
Up and Running with Reinforcement Learning
Why RL?
The relationship between an agent and its environment
Identifying episodes
Identifying reward functions and the concept of discounted rewards
Learning the Markov decision process
Defining the Bellman equation
On-policy versus off-policy learning
Model-free and model-based training
Algorithms covered in this book
Summary
Questions
Further reading
2
Temporal Difference, SARSA, and Q-Learning
Temporal Difference, SARSA, and Q-Learning
Technical requirements
Understanding TD learning
Understanding SARSA and Q-Learning
Cliff walking and grid world problems
Summary
Further reading
3
Deep Q-Network
Deep Q-Network
Technical requirements
Learning the theory behind a DQN
Understanding target networks
Learning about replay buffer
Getting introduced to the Atari environment
Summary
Questions
Further reading
4
Double DQN, Dueling Architectures, and Rainbow
Double DQN, Dueling Architectures, and Rainbow
Technical requirements
Understanding Double DQN
Understanding dueling network architectures
Understanding Rainbow networks
Running a Rainbow network on Dopamine
Summary
Questions
Further reading
5
Deep Deterministic Policy Gradient
Deep Deterministic Policy Gradient
Technical requirements
Actor-Critic algorithms and policy gradients
Deep Deterministic Policy Gradient
Training and testing the DDPG on Pendulum-v0
Summary
Questions
Further reading
6
Asynchronous Methods - A3C and A2C
Asynchronous Methods - A3C and A2C
Technical requirements
The A3C algorithm
The A3C algorithm applied to CartPole
The A3C algorithm applied to LunarLander
The A2C algorithm
Summary
Questions
Further reading
7
Trust Region Policy Optimization and Proximal Policy Optimization
Trust Region Policy Optimization and Proximal Policy Optimization
Technical requirements
Learning TRPO
Learning PPO
Using PPO to solve the MountainCar problem
Evaluating the performance
Summary
Questions
Further reading
8
Deep RL Applied to Autonomous Driving
Deep RL Applied to Autonomous Driving
Technical requirements
Car driving simulators
Learning to use TORCS
Training a DDPG agent to learn to drive
Training a PPO agent
Summary
Questions
Further reading
9
Assessment
Assessment
Chapter 1
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
10
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Summary

In this chapter, we introduced the A3C algorithm, which is an on-policy algorithm that's applicable to both discrete and continuous action problems. You saw how three different loss terms are combined into one and optimized. Python's threading library is useful for running multiple threads, with a copy of the policy network in each thread. These different workers compute the policy gradients and pass them on to the master to update the neural network parameters. We applied A3C to train agents for the CartPole and the LunarLander problems, and the agents learned them very well. A3C is a very robust algorithm and does not require a replay buffer, although it does require a local buffer for collecting a small number of experiences, after which it is used to update the networks. Lastly, a synchronous version of the algorithm, called A2C, was also introduced.

This...

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