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Machine Learning

This repository contains roughly implemented codes so not so well documented.

Introduction

Maximum files in this repository are written in python. These files try to train an agent to play games. The gaming environment provided by OpenAI. OpenAIGym provides 2D games environment and we used that.

OpenAIGym

You can find the documentation and details here -

Work

The algorithms that are used here to train agents are value iteration, policy iteration, policy evaluation, Q-learning etc. And the game environment we tried are frozenLake and CartPole. We will try other environments and update this repository.

Frozen Lake

This is a toy text game. Details - * Frozen Lake

Our Policy Evaluation Solution

Cart Pole

This is a classic game. Details - * Cart Pole

Our Q-Learning Solution

Authors

  • Syed Intiser Ahsan

Acknowledgments

  • Monirul Hasan Khan ( Co-ordinator, CSE Department of Southeast University)
  • Kazi Fozle Azim Rabi
  • Sakib Shahriyar Pathan

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