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

State estimation

If we expand the state spaces, this helps us to convert the POMDP into an MDP where Z contains fully observable state space. This gives the notion of belief state b(s), which is the state that the decision maker is going to use in the context of a POMDP . The belief state, that is, b(s) gives the probability of the agent being in the s state. Therefore, belief state, b, is a vector representing the probability distribution over all states. Thus, the belief state gets updated as soon as an action is taken.

Say, there's a belief state, b, the agent takes an action, a, and received some observations, z. This forms a new belief state. Therefore, we are converting a POMDP to belief MDP where it will consist of belief states as ...

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