Iterative Policy Evaluation Algorithm in Python and OpenAI Gym – Reinforcement Learning Tutorial
#machinelearning #reinforcementlearning #reinforcement #machinelearningtutorial #machinelearningengineer #datascience #datasciencecareer #datasciencetutorial #controlengineering #controltheory #controltheory #controlsystems #dynamicprogramming #dynamicalsystems #signalprocessing #datascienceworkshop #programming #python #openai
It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way:
- Buy me a Coffee: https://www.buymeacoffee.com/AleksandarHaber
- PayPal: https://www.paypal.me/AleksandarHaber
- Patreon: https://www.patreon.com/user?u=32080176&fan_landing=true
- You Can also press the Thanks YouTube Dollar button
The post accompanying this video is given here:
https://aleksandarhaber.com/iterative-policy-evaluation-algorithm-in-python-reinforcement-learning-tutorial/
In this reinforcement learning tutorial, we explain the iterative policy evaluation algorithm and we explain how to implement this algorithm in Python and OpenAI Gym. We use Frozen Lake OpenAI Gym environment to test the performance of the iterative policy evaluation algorithm. This video is a part of a larger series of tutorials on reinforcement learning.
The iterative policy evaluation algorithm is used to iteratively compute the value function. The idea is to apply the fixed point iteration method to the Bellman equation in order to iteratively compute the value function. We provide a detailed explanations of the basic ideas of the iterative policy evaluation algorithm, as well as how to implement this algorithm in Python.
#machinelearning #reinforcementlearning #reinforcement #machinelearningtutorial #machinelearningengineer #datascience #datasciencecareer #datasciencetutorial #controlengineering #controltheory #controltheory #controlsystems #dynamicprogramming #dynamicalsystems #signalprocessing #datascienceworkshop #programming #python #openai
It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way:
– Buy me a Coffee: https://www.buymeacoffee.com/AleksandarHaber
– PayPal: https://www.paypal.me/AleksandarHaber
– Patreon: https://www.patreon.com/user?u=32080176&fan_landing=true
– You Can also press the Thanks YouTube Dollar button
The post accompanying this video is given here:
https://aleksandarhaber.com/iterative-policy-evaluation-algorithm-in-python-reinforcement-learning-tutorial/
In this reinforcement learning tutorial, we explain the iterative policy evaluation algorithm and we explain how to implement this algorithm in Python and OpenAI Gym. We use Frozen Lake OpenAI Gym environment to test the performance of the iterative policy evaluation algorithm. This video is a part of a larger series of tutorials on reinforcement learning.
The iterative policy evaluation algorithm is used to iteratively compute the value function. The idea is to apply the fixed point iteration method to the Bellman equation in order to iteratively compute the value function. We provide a detailed explanations of the basic ideas of the iterative policy evaluation algorithm, as well as how to implement this algorithm in Python.
