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

Author (1)

Palanisamy P

Implementing the RL agent’s runtime components

We have looked at several agent algorithm implementations in the previous chapters. You may have noticed from recipes in the previous chapters (especially Chapter 3, Implementing Advanced Deep RL Algorithms), where we implemented RL agent training code, that some parts of the agent code were conditionally executed. For example, the experience replay routine was only run when a certain condition (such as the number of samples in the replay memory) was met, and so on. That begs the question: what are the essential components in an agent that is required, especially when we do not aim to train it further and only execute a learned policy?

This recipe will help you distill the implementation of the Soft Actor-Critic (SAC) agent down to the minimal set of components – those that are absolutely necessary for the runtime of your agent.

Let’s get started!

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TensorFlow 2 Reinforcement Learning Cookbook

Published in: Jan 2021Publisher: PacktISBN-13: 9781838982546

Author (1)

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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You're reading from TensorFlow 2 Reinforcement Learning Cookbook

Implementing the RL agent’s runtime components

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Author (1)

Hands-On Intelligent Agents with OpenAI Gym

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