Skip to Content
Reinforcement Learning with TensorFlow
book

Reinforcement Learning with TensorFlow

by Sayon Dutta
April 2018
Intermediate to advanced content levelIntermediate to advanced
334 pages
10h 18m
English
Packt Publishing
Content preview from Reinforcement Learning with TensorFlow

Model based learning and model free learning

In Chapter 3Markov Decision Process, we used states, actions, rewards, transition models, and discount factors to solve our Markov decision process, that is, the MDP problem. Thus, if all these elements of an MDP problem are available, we can easily use a planning algorithm to come up with a solution to the objective. This type of learning is called model based learning, where an AI agent will interact with the environment and based on its interactions, will try to approximate the environment's model, that is, the state transition model. Given the model, now the agent can try to find the optimum policy through value iteration or policy iteration.

But its not necessary for our AI agent to learn ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Deep Learning with TensorFlow - Second Edition

Deep Learning with TensorFlow - Second Edition

Giancarlo Zaccone, Vihan Jain, Md. Rezaul Karim, Motaz Saad
Deep Learning with TensorFlow 2 and Keras - Second Edition

Deep Learning with TensorFlow 2 and Keras - Second Edition

Antonio Gulli, Dr. Amita Kapoor, Sujit Pal

Publisher Resources

ISBN: 9781788835725Supplemental Content