Learn how to build a custom reinforcement learning environment using OpenAI Gym and Python in this 25-minute tutorial. Discover the process of creating a basic custom environment class, including setting up the __init__, step, and reset methods. Train a simple RL model using Keras-RL to interact with your custom environment. Follow along as the instructor demonstrates cloning baseline code, designing a custom environment blueprint, installing dependencies, and implementing key methods. Test your custom environment, train a DQN agent, and run it on your newly created setup. Gain practical skills for developing specific Python RL environments tailored to your projects in the field.
Building a Custom Environment for Deep Reinforcement Learning with OpenAI Gym and Python
Overview
Syllabus
- Start
- Cloning Baseline Reinforcement Learning Code
- Custom Environment Blueprint and Scenario
- Installing and Importing Dependencies
- Creating a Custom Environment with OpenAI Gym
- Coding the __init__ method for a OpenAI Environment
- Coding the step method for an OpenAI Environment
- Coding the reset method for an OpenAI Environment
- Testing a Custom OpenAI Environment
- Training a DQN Agent with Keras-RL
- Running a DQN Agent on a Custom Environment using Keras-RL
Taught by
Nicholas Renotte
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