June 2025
Intermediate to advanced
400 pages
10h 37m
English
book
Distributed Time-Sensitive Systems
by Tanupriya Choudhury, Rahul Kumar Singh, Ravi Tomar, S. Balamurugan, J. C. Patni
Content preview from Distributed Time-Sensitive Systems
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
9Using Reinforcement Learning in Unity Environments for Training AI-Agent
Geetika Munjal1 and Monika Lamba2*
1Amity University, Noida, Uttar Pradesh, India
2The NorthCap University, Gurugram, Haryana, India
Abstract
An intelligent AI-Agent that can carry out a variety of tasks in a virtual environment is created using reinforcement learning. This chapter will demonstrate how machine learning and artificial intelligence techniques can be used to deploy a single AI-Agent to carry out a variety of tasks in various settings. As a result, it is possible to use a single AI-Agent to address a variety of problems, doing away with the need to develop separate, highly specialized AI-Agents for each distinct problem encountered in a different Environment. AI-Agent has been transformed into a reusable entity that can be used to train in various scenarios and effectively learn how to solve the specific problem presented by each one. AI-Agents are more resilient and easily adaptable in changing environments when they have the ability to solve multiple problems with a single AI-Agent. In the end, this would decrease and optimize the cost of resources (such as time, money, and energy), as well as increase and optimize human innovation. The Unity 3D Engine, Python, PyTorch, and ML-Agents are the main resource requirements to be used in this chapter.
Keywords: Reinforcement learning, AI-Agent, machine learning, Artificial Intelligence, unity, environment
9.1 Introduction
Machine learning ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access
More than 5,000 organizations count on O’Reilly
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.