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You're reading from  TensorFlow 2 Reinforcement Learning Cookbook

Product typeBook
Published inJan 2021
Reading LevelExpert
PublisherPackt
ISBN-139781838982546
Edition1st Edition
Languages
Python
Tools
TensorFlow
Concepts
Reinforcement Learning
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Author (1)
Palanisamy P
Palanisamy P
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Palanisamy P

Praveen Palanisamy works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R&D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.
Read more about Palanisamy P

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Testing/evaluating RL agents

Let’s assume that you have trained the SAC agent in one of the trading environments using the training script (previous recipe) and that you have several versions of the trained agent models, each with different policy network architectures or hyperparameters or your own tweaks and customizations to improve its performance. When you want to deploy an agent, you want to make sure that you pick the best performing agent, don’t you?

This recipe will help you build a lean script to evaluate a given pre-trained agent model locally so that you can get a quantitative performance assessment and compare several trained models before choosing the right agent model for deployment. Specifically, we will use the tradegym module and the sac_agent_runtime module that we built earlier in this chapter to evaluate the agent models that we train.

Let’s get started!

Getting ready

To complete this recipe, you will first need to activate the...

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TensorFlow 2 Reinforcement Learning Cookbook
Published in: Jan 2021Publisher: PacktISBN-13: 9781838982546
© 2021 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Palanisamy P

Praveen Palanisamy works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R&D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.
Read more about Palanisamy P

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