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.
Table of Contents (11 chapters)

Summary

In this chapter, we were introduced to DDQN, dueling network architectures, and the Rainbow DQN. We extended our previous DQN code to DDQN and dueling architectures and tried it out on Atari Breakout. We can clearly see that the average episode rewards are higher with these improvements, and so these improvements are a natural choice to use. Next, we also saw Google's Dopamine and used it to train a Rainbow DQN agent. Dopamine has several other RL algorithms, and the user is encouraged to dig deeper and try out these other RL algorithms as well.

This chapter was a good deep dive into the DQN variants, and we really covered a lot of mileage as far as coding of RL algorithms is involved. In the next chapter, we will learn about our next RL algorithm called Deep Deterministic Policy Gradient (DDPG), which is our first Actor-Critic RL algorithm and our first continuous...