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This repository contains the lab material for the Reinforcement Learning F22 course prepared for Innopolis University Master's students.

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IU-Reinforcement-Learning-22-lab

Week01 - Introduction Open In Colab

By the end of this lab you are expected to:

  • Have a quick review for the lecture 01.
  • Understand the fundamental concepts of reinforcement learning and learn to test your algorithms with OpenAI gym.
  • Training your first reinforcement learning model using stable-baselines3 , evaluate, test, use callbacks and learn how to save and load the RL models.

Week02 - Exploration and exploitation Open In Colab

In this lab you will implement several exploration strategies for simplest problem - bernoulli bandit.

Week03 - Markov Decision Process Open In Colab

By the end of this lab you will understand how Markov Reward Process and Markov Decision Process work. Also you will apply the direct solution to find the optimal policy.

Week04 - Dynamic Programming Open In Colab

Policy Iteration and Value Iteration algorithms.

Week05 - Model-Free Reinforcement Learning (MC Prediction) Open In Colab

Monte Carlo for Prediction.

Week06 - Model-Free Reinforcement Learning (TD Prediction) Open In Colab

Temporal Difference Prediction

Week07 - Model-Free Reinforcement Learning (Model-Free Control) Open In Colab

By the end of this lab you will understand the difference between Model-Free Prediction and Model-Free Control and you will be familiar with On-Policy vs Off-Policy Learning, and you will implement SARSA & Q-Learning algorithms from scratch.

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This repository contains the lab material for the Reinforcement Learning F22 course prepared for Innopolis University Master's students.

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