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Introduction to OpenAI Gym (Gymnasium): Cart-Pole Environment - Reinforcement Learning Tutorial
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#reinforcementlearning #machinelearning #reinforcementlearningtutorial #controlengineering #controltheory #controlsystems #pythontutorial #python #openai #openaigym
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In this OpenAI Gym tutorial, we introduce a Cart Pole OpenAI Gym (or Gymnasium) environment. Cart Pole environment is important since it is a classical control engineering environment. We explain how to create an environment and how to simulate random episodes. We example the state variables and rewards. In the next video, we explain how to test the Q-Learning algorithm on this environment.
If you need help with your professional engineering problem, or you need to develop new skills in the fields of control, signal processing, embedded systems, programming, optimization, machine learning, robotics, etc., we are here to help. We provide engineering services, as well as tutoring and skill development services. We have significant industry, research, and university-level teaching experience. Describe your problem and we will send you a quote for our services.
It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way:
- You Can also press the Thanks YouTube Dollar button
The webpage tutorial accompanying this video tutorial is given here:
In this OpenAI Gym tutorial, we introduce a Cart Pole OpenAI Gym (or Gymnasium) environment. Cart Pole environment is important since it is a classical control engineering environment. We explain how to create an environment and how to simulate random episodes. We example the state variables and rewards. In the next video, we explain how to test the Q-Learning algorithm on this environment.
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