Learn to build a custom environment for Deep Reinforcement Learning with OpenAI Gym and Python in just 25 minutes. This video tutorial will guide you through creating a basic custom reinforcement learning environment, setting up essential methods, and training a simple RL model using Python, Keras-RL, and OpenAI Gym. By the end, you will be able to build a custom environment, train a DQN Agent, and test a Reinforcement Learning agent on your custom environment. The course is designed for those interested in advancing their skills in reinforcement learning and Python programming.
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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