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

Asynchronous one-step SARSA

The architecture of asynchronous one-step SARSA is almost similar to the architecture of asynchronous one-step Q-learning, except the way target state-action value of the current state is calculated by the target network. Instead of using the maximum Q-value of the next state s' by the target network, SARSA uses 

-greedy to choose the action a' for the next state s' and the Q-value of the next state action pair, that is, Q(s',a';

) is used to calculate the target state-action value of the current state. 

The pseudo-code for asynchronous one-step SARSA is shown below. Here, the following are the global parameters:

  •  : the parameters (weights and biases) of the policy network
  •  : parameters (weights and biases) of the target network  
  • T : overall time step counter 
// Globally shared parameters 
and T //
is initialized arbitrarily // T is initialized 0 pseudo-code for each learner running parallel in each of the threads: Initialize thread level time step counter t=0...