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

Training the FrozenLake-v0 environment using MDP

This is about a gridworld environment in OpenAI gym called FrozenLake-v0, discussed in Chapter 2, Training Reinforcement Learning Agents Using OpenAI Gym. We implemented Q-learning and Q-network (which we will discuss in future chapters) to get the understanding of an OpenAI gym environment.

Now, let's try to implement value iteration to obtain the utility value of each state in the FrozenLake-v0 environment, using the following code:

# importing dependency libraries
from __future__ import print_function
import gym
import numpy as np
import time

#Load the environment
env = gym.make('FrozenLake-v0')

s = env.reset()


print(env.action_space) #number of actions
print(env.observation_space) #number of states

print("Number of actions : ",env.action_space.n)
print("Number of states : ",env.observation_space.n)

# Value Iteration Implementation

#Initializing Utilities of all states with zeros...