OpenAI Gym Training Course
OpenAI Gym is an open-source interface designed to create, develop, and compare reinforcement learning (RL) tasks and algorithms. It offers a comprehensive set of environment libraries that simplify the execution of reinforcement learning tasks.
This instructor-led, live training (available online or onsite) is designed for researchers and developers who want to install, configure, customize, and implement OpenAI Gym to rapidly develop reinforcement learning algorithms.
Upon completion of this training, participants will be able to build, develop, execute, and test reinforcement learning algorithms to optimize tasks and achieve maximum results.
Format of the Course also allows for the evaluation of participants.
- Interactive lecture and discussion.
- Extensive exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction
Overview of OpenAI Gym Features and Architecture
Learning About OpenAI Gym Basics
Exploring the OpenAI Gym Library
Installing OpenAI Gym and Dependencies
Building Gym Directly from Source
Working with OpenAI Gym Environments
Creating Your Own Environments
Executing Environment Functions and Tasks
Working with Agents, Actions, Observations, and Rewards
Understanding Space Types in Environments
Using OpenAI Gym’s Registry Function
Testing the Algorithms and Environments
Troubleshooting
Summary and Conclusion
Requirements
- Knowledge of Python or other programming languages
- Understanding of artificial intelligence (AI) and machine learning (ML) concepts
Audience
- Researchers
- Developers
Open Training Courses require 5+ participants.
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