Book Image

Reinforcement Learning with TensorFlow

By : Sayon Dutta
Book Image

Reinforcement Learning with TensorFlow

By: Sayon Dutta

Overview of this book

Reinforcement learning (RL) allows you to develop smart, quick and self-learning systems in your business surroundings. It's an effective method for training learning agents and solving a variety of problems in Artificial Intelligence - from games, self-driving cars and robots, to enterprise applications such as data center energy saving (cooling data centers) and smart warehousing solutions. The book covers major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You'll also be introduced to the concept of reinforcement learning, its advantages and the reasons why it's gaining so much popularity. You'll explore MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, and temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have gained a firm understanding of what reinforcement learning is and understand how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.
Table of Contents (21 chapters)
Title Page
Packt Upsell

Chapter 2. Training Reinforcement Learning Agents Using OpenAI Gym

The OpenAI Gym provides a lot of virtual environments to train your reinforcement learning agents. In reinforcement learning, the most difficult task is to create the environment. This is where OpenAI Gym comes to the rescue, by providing a lot of toy game environments to provide users with a platform to train and benchmark their reinforcement learning agents.

In other words, it provides a playground for the reinforcement learning agent to learn and benchmark their performance, where the agent has to learn to navigate from the start state to the goal state without undergoing any mishaps.

Thus, in this chapter, we will be learning to understand and use environments from OpenAI Gym and trying to implement basic Q-learning and the Q-network for our agents to learn.

OpenAI Gym provides different types of environments. They are as follows:

  • Classic control
  • Algorithmic
  • Atari
  • Board games
  • Box2D
  • Parameter tuning
  • MuJoCo
  • Toy text
  • Safety
  • Minecraft...