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In this assignment, you will gain experience working with OpenAI Gym, which is a set of problems that can be explored with different reinforcement learning algorithms. This assignment is designed to help you apply the concepts you have been learning about Q-learning algorithms to the “cartpole” problem, a common reinforcement learning problem.
Note: The original code referenced in this assignment was written in Python 2.x. You have been given a zipped folder containing an updated Python 3 version of the code that will work in the Apporto environment. To make this code work, some lines have been commented out. Please leave these as comments.
Reference: Surma, G. (2018). Cartpole. Github repository. Retrieved from https://github.com/gsurma/cartpole.
Access the Virtual Lab (Apporto) by using the link in the Virtual Lab Access module. It is recommended that you use the Chrome browser to access the Virtual Lab. If prompted to allow the Virtual Lab access to your clipboard, click “Yes”, as this will allow you to copy text from your desktop into applications in the Virtual Lab environment.
Note: The Cartpole folder contains the Cartpole.ipynb file (Jupyter Notebook) and a scores folder containing score_logger.py (Python file). It is very important to keep the score_logger.py file in the scores folder (directory).
Thus, if your name is Jane Doe, please name the submission file “Doe_Jane_Assignment5.ipynb”.
Solved in _ runs, _ total runs.
Note: If you receive the error “NameError: name ‘exit’ is not defined” after the above line, you can ignore it.
Note: Discount factor = GAMMA, learning rate = LEARNING_RATE, exploration factor = combination of EXPLORATION_MAX, EXPLORATION_MIN, and EXPLORATION_DECAY.
Specifically, you must address the following rubric criteria:
Please submit your completed IPYNB file. Make sure that your file is named as specified above, and that you have addressed all rubric criteria in your response. Sources should be cited in APA style.
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