OpenAI Gym Training Course
OpenAI Gym is an open-source interface used to create, develop, and compare reinforcement learning (RL) tasks and algorithms. Gym provides a wide set of environment libraries to run reinforcement learning tasks with ease.
This instructor-led, live training (online or on-site) is designed for researchers and developers who wish to install, configure, customise, and implement OpenAI Gym to rapidly develop reinforcement learning algorithms.
By the end of this training, participants will be able to build, develop, execute, and test reinforcement learning algorithms to optimise tasks and achieve maximum results.
Course Format
- Interactive lectures and discussions.
- Numerous exercises and practical sessions.
- Hands-on implementation in a live-lab environment.
Course Customisation Options
- To request a customised version of this course, please contact us to arrange.
Course Outline
Introduction
Overview of OpenAI Gym Features and Architecture
Learning the Basics of OpenAI Gym
Exploring the OpenAI Gym Library
Installing OpenAI Gym and Its 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 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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Provisional Upcoming Courses (Require 5+ participants)
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