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on
1
- Start
2
- Cloning Baseline Reinforcement Learning Code
3
- Custom Environment Blueprint and Scenario
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- Installing and Importing Dependencies
5
- Creating a Custom Environment with OpenAI Gym
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- Coding the __init__ method for a OpenAI Environment
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- Coding the step method for an OpenAI Environment
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- Coding the reset method for an OpenAI Environment
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- Testing a Custom OpenAI Environment
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- Training a DQN Agent with Keras-RL
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- Running a DQN Agent on a Custom Environment using Keras-RL
Description:
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

Nicholas Renotte
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