You're reading from Reinforcement Learning Algorithms with Python

Product typeBook

Published inOct 2019

Reading LevelBeginner

PublisherPackt

ISBN-139781789131116

Edition1st Edition

Languages

Python

Concepts

Reinforcement Learning

Author (1)

Andrea Lonza

Applying SARSA to Taxi-v2

After a more theoretical view of TD learning and particularly of SARSA, we are finally able to implement SARSA to solve problems of interest. As we saw previously, SARSA can be applied to environments with unknown models and dynamics, but as it is a tabular algorithm with scalability constraints, it can only be applied to environments with small and discrete action and state spaces. So, we choose to apply SARSA to a gym environment called Taxi-v2 that satisfies all the requirements and is a good test bed for these kinds of algorithm.

Taxi-v2 is a game that was introduced to study hierarchical reinforcement learning (a type of RL algorithm that creates a hierarchy of policies, each with the goal of solving a subtask) where the aim is to pick up a passenger and drop them at a precise location. A reward of +20 is earned when the taxi performs a successful...

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Reinforcement Learning Algorithms with Python

Published in: Oct 2019Publisher: PacktISBN-13: 9781789131116

Author (1)

Andrea Lonza

Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.
Read more about Andrea Lonza

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Author (1)

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