Building a Custom Environment for Deep Reinforcement Learning with OpenAI Gym and Python

Tired of working with standard OpenAI Environments?

Want to get started building your own custom Reinforcement Learning Environments?

Need a specific Python RL environment built for a project you’re working on in the field?

In this video you’ll learn how to do exactly that in 25 minutes. In this video you’ll learn how to build a basic custom reinforcement learning environment to get started with reinforcement learning. We’ll go through how to build your own environment class, setting up the __init__, step and reset methods and then train a simple RL model to learn how to interact with it using Python, Keras-RL and OpenAI Gym.

In this video you’ll go through:1. How to build a  custom environment with OpenAI  Gym2. Training a DQN Agent on a Custom OpenAI Environment3. Testing out a Reinforcement Learning agent on a Custom Environment

Get the CODE: https://github.com/nicknochnack/OpenAI-Reinforcement-Learning-with-Custom-Environment

Chapters0:00 – Start0:30 – Cloning Baseline Reinforcement Learning Code3:12 – Custom Environment Blueprint and Scenario5:22 – Installing and Importing Dependencies7:44 – Creating a Custom Environment with OpenAI Gym9:21 – Coding the __init__() method for a OpenAI Environment12:26 – Coding the step() method for an OpenAI Environment16:50 – Coding the reset() method for an OpenAI Environment17:23 – Testing a Custom OpenAI Environment20:29 – Training a DQN Agent with Keras-RL23:48 – Running a DQN Agent on a Custom Environment using Keras-RL

Oh, and don’t forget to connect with me!LinkedIn: https://www.linkedin.com/in/nicholasrenotteFacebook: https://www.facebook.com/nickrenotte/GitHub: https://github.com/nicknochnack

Happy coding!Nick

P.s. Let me know how you go and drop a comment if you need a hand!Tired of working with standard OpenAI Environments?

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