You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook

Published inJan 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838826994

Edition2nd Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Deep Reinforcement Learning

Author (1)

Maxim Lapan

A2C on Pong

In the previous chapter, you saw a (not very successful) attempt to solve our favorite Pong environment with policy gradient methods. Let's try it again with the actor-critic method at hand.

GAMMA = 0.99
LEARNING_RATE = 0.001
ENTROPY_BETA = 0.01
BATCH_SIZE = 128
NUM_ENVS = 50
REWARD_STEPS = 4
CLIP_GRAD = 0.1

We start, as usual, by defining hyperparameters (imports are omitted). These values are not tuned, as we will do this in the next section of this chapter. We have one new value here: CLIP_GRAD. This hyperparameter specifies the threshold for gradient clipping, which basically prevents our gradients from becoming too large at the optimization stage and pushing our policy too far. Clipping is implemented using the PyTorch functionality, but the idea is very simple: if the L2 norm of the gradient is larger than this hyperparameter, then the gradient vector is clipped to this value.

The REWARD_STEPS hyperparameter determines how many...

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Deep Reinforcement Learning Hands-On. - Second Edition

Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994

Author (1)

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

A2C on Pong

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Author (1)

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