Deep Q Learning with TensorFlow and OpenAI Gym

Overview

This repository contains code files that implement the Deep Q Learning Network algorithm from scratch using Python, TensorFlow, and OpenAI Gym. The implementation is specifically tested in the OpenAI Gym Cart Pole (v1) environment, showcasing the practical application of reinforcement learning techniques.

Key Features

• Algorithm Implementation: Full implementation of the Deep Q Learning algorithm.
• Frameworks Used: Built using Python and TensorFlow, leveraging their powerful libraries for machine learning.
• Testing Environment: The code is tested in the OpenAI Gym Cart Pole (v1) environment, providing a standard benchmark for reinforcement learning tasks.

Use Cases

• Educational Purposes: Ideal for students and practitioners looking to understand reinforcement learning concepts.
• Research: Useful for researchers exploring variations of the Deep Q Learning algorithm.
• Development: Can be used as a foundation for developing more complex reinforcement learning applications.