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Abhishek Nandy - Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python

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Book:
Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python
Author:
Abhishek Nandy / Manisha Biswas
Publisher:
Apress
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Books / Computer
Year:
2017
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Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. Youll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process.

Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markovs Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI before looking at Open AI Gym. Youll then learn about Swarm Intelligence with Python in terms of reinforcement learning.

The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. Theres also coverage of Keras, a framework that can be used with reinforcement learning. Finally, youll delve into Googles Deep Mind and see scenarios where reinforcement learning can be used.

What Youll Learn

Absorb the core concepts of the reinforcement learning process
Use advanced topics of deep learning and AI
Work with Open AI Gym, Open AI, and Python
Harness reinforcement learning with TensorFlow and Keras using Python

Who This Book Is For

Data scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning.

Abhishek Nandy: author's other books

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Abhishek Nandy and Manisha Biswas 2018

Abhishek Nandy and Manisha Biswas Reinforcement Learning

1. Reinforcement Learning Basics

Abhishek Nandy 1 and Manisha Biswas 2

(1)

Rm HIG L-2/4, Bldg Swaranika Co-Opt HSG, Kolkata, West Bengal, India

(2)

North 24 Parganas, West Bengal, India

This chapter is a brief introduction to Reinforcement Learning (RL) and includes some key concepts associated with it.

In this chapter, we talk about Reinforcement Learning as a core concept and then define it further. We show a complete flow of how Reinforcement Learning works. We discuss exactly where Reinforcement Learning fits into artificial intelligence (AI). After that we define key terms related to Reinforcement Learning. We start with agents and then touch on environments and then finally talk about the connection between agents and environments.

What Is Reinforcement Learning?

We use Machine Learning to constantly improve the performance of machines or programs over time. The simplified way of implementing a process that improves machine performance with time is using Reinforcement Learning (RL). Reinforcement Learning is an approach through which intelligent programs, known as agents , work in a known or unknown environment to constantly adapt and learn based on giving points. The feedback might be positive, also known as rewards , or negative, also called punishments . Considering the agents and the environment interaction, we then determine which action to take.

In a nutshell, Reinforcement Learning is based on rewards and punishments .

Some important points about Reinforcement Learning:

It differs from normal Machine Learning, as we do not look at training datasets.
Interaction happens not with data but with environments, through which we depict real-world scenarios.
As Reinforcement Learning is based on environments, many parameters come in to play. It takes lots of information to learn and act accordingly.
Environments in Reinforcement Learning are real-world scenarios that might be 2D or 3D simulated worlds or game-based scenarios .
Reinforcement Learning is broader in a sense because the environments can be large in scale and there might be a lot of factors associated with them.
The objective of Reinforcement Learning is to reach a goal.
Rewards in Reinforcement Learning are obtained from the environment.

The Reinforcement Learning cycle is depicted in Figure with the help of a robot.

Figure 1-1.

Reinforcement Learning cycle

A maze is a good example that can be studied using Reinforcement Learning , in order to determine the exact right moves to complete the maze (see Figure ).

Figure 1-2.

Reinforcement Learning can be applied to mazes

In Figure , we are applying Reinforcement Learning and we call it the Reinforcement Learning box because within its vicinity the process of RL works. RL starts with an intelligent program, known as agents, and when they interact with environments, there are rewards and punishments associated. An environment can be either known or unknown to the agents. The agents take actions to move to the next state in order to maximize rewards.

Figure 1-3.

Reinforcement Learning flow

In the maze, the centralized concept is to keep moving. The goal is to clear the maze and reach the end as quickly as possible.

The following concepts of Reinforcement Learning and the working scenario are discussed later this chapter.

The agent is the intelligent program
The environment is the maze
The state is the place in the maze where the agent is
The action is the move we take to move to the next state
The reward is the points associated with reaching a particular state. It can be positive, negative, or zero

We use the maze example to apply concepts of Reinforcement Learning. We will be describing the following steps :

The concept of the maze is given to the agent.
There is a task associated with the agent and Reinforcement Learning is applied to it.
The agent receives (a-1) reinforcement for every move it makes from one state to other.
There is a reward system in place for the agent when it moves from one state to another.

The rewards predictions are made iteratively, where we update the value of each state in a maze based on the value of the best subsequent state and the immediate reward obtained. This is called the update rule.

The constant movement of the Reinforcement Learning process is based on decision-making.

Reinforcement Learning works on a trial-and-error basis because it is very difficult to predict which action to take when it is in one state. From the maze problem itself, you can see that in order get the optimal path for the next move, you have to weigh a lot of factors. It is always on the basis of state action and rewards. For the maze, we have to compute and account for probability to take the step.

The maze also does not consider the reward of the previous step; it is specifically considering the move to the next state. The concept is the same for all Reinforcement Learning processes.

Here are the steps of this process:

We have a problem.
We have to apply Reinforcement Learning.
We consider applying Reinforcement Learning as a Reinforcement Learning box.
The Reinforcement Learning box contains all essential components needed for applying the Reinforcement Learning process.
The Reinforcement Learning box contains agents, environments, rewards, punishments, and actions.

Reinforcement Learning works well with intelligent program agents that give rewards and punishments when interacting with an environment.

The interaction happens between the agents and the environments, as shown in Figure .

Figure 1-4.

Interaction between agents and environments

From Figure , you can see that there is a direct interaction between the agents and its environments. This interaction is very important because through these exchanges, the agent adapts to the environments. When a Machine Learning program, robot, or Reinforcement Learning program starts working, the agents are exposed to known or unknown environments and the Reinforcement Learning technique allows the agents to interact and adapt according to the environments features.

Accordingly, the agents work and the Reinforcement Learning robot learns. In order to get to a desired position, we assign rewards and punishments.

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