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

The SARSA algorithm

The State–Action–Reward–State–Action (SARSA) algorithm is an on-policy learning problem. Just like Q-learning, SARSA is also a temporal difference learning problem, that is, it looks ahead at the next step in the episode to estimate future rewards. The major difference between SARSA and Q-learning is that the action having the maximum Q-value is not used to update the Q-value of the current state-action pair. Instead, the Q-value of the action as the result of the current policy, or owing to the exploration step like 

-greedy is chosen to update the Q-value of the current state-action pair. The name SARSA comes from the fact that the Q-value update is done by using a quintuple Q(s,a,r,s',a') where: 

  • s,a: current state and action
  • r: reward observed post taking action a
  • s': next state reached after taking action a
  • a': action to be performed at state s'

Steps involved in the SARSA algorithm are as follows:

  1. Initialize Q-table randomly

  2. For each episode:

    1. For the given state s, choose...