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You're reading from  Statistics for Machine Learning

Product typeBook
Published inJul 2017
Reading LevelIntermediate
PublisherPackt
ISBN-139781788295758
Edition1st Edition
Languages
Python
Concepts
Statistics
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Pratap Dangeti
Pratap Dangeti
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Pratap Dangeti

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
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SARSA on-policy TD control

State-action-reward-state-action (SARSA) is an on-policy TD control problem, in which policy will be optimized using policy iteration (GPI), only time TD methods used for evaluation of predicted policy. In the first step, the algorithm learns an SARSA function. In particular, for an on-policy method we estimate qπ (s, a) for the current behavior policy π and for all states (s) and actions (a), using the TD method for learning vπ. Now, we consider transitions from state-action pair to state-action pair, and learn the values of state-action pairs:

This update is done after every transition from a non-terminal state St. If St+1 is terminal, then Q (St+1, At+1) is defined as zero. This rule uses every element of the quintuple of events (St, At, Rt, St+1, At+1), which make up a transition from one state-action pair to the next. This quintuple gives rise to the name SARSA for the algorithm.

As in all on-policy methods, we continually estimate qπ for the behavior policy...

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Statistics for Machine Learning
Published in: Jul 2017Publisher: PacktISBN-13: 9781788295758
© 2017 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Pratap Dangeti

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
Read more about Pratap Dangeti

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