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

Training code

We have two very similar training modules in this example: one for the feed-forward model and one for 1D convolutions. For both of them, there is nothing new added to our examples from Chapter 8, DQN Extensions:

They're using epsilon-greedy action selection to perform exploration. The epsilon linearly decays over the first 1M steps from 1.0 to 0.1.
A simple experience replay buffer of size 100k is being used, which is initially populated with 10k transitions.
For every 1,000 steps, we calculate the mean value for the fixed set of states to check the dynamics of the Q-values during the training.
For every 100k steps, we perform validation: 100 episodes are played on the training data and on previously unseen quotes. Characteristics of orders are recorded in TensorBoard, such as the mean profit, the mean count of bars, and the share held. This step allows us to check for overfitting conditions.

The training modules are in Chapter10/train_model...

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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

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Author (1)

TensorFlow Reinforcement Learning Quick Start Guide

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