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 scalable ES to LunarLander

How well will the scalable version of evolution strategies perform in the LunarLander environment? Let's find out!

As you may recall, we already used LunarLander against A2C and REINFORCE in Chapter 6, Learning Stochastic and PG optimization. This task consists of landing a lander on the moon through continuous actions. We decided to use this environment for its medium difficulty and to compare the ES results to those that were obtained with A2C.

The hyperparameters that performed the best in this environment are as follows:

Hyperparameter	Variable name	Value
Neural network size	`hidden_sizes`	[32, 32]
Training iterations (or generations)	`number_iter`	200
Worker's number	`num_workers`	4
Adam learning rate	`lr`	0.02
Individuals per worker	`indiv_per_worker`	12
Standard deviation	`std_noise`	0.05

The results are shown in the...

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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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