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rl-gym-from-scratch
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# Deep Reinforcement Learning Gym From Scratch ## Overview This project is a Python-based implementation of a Deep Reinforcement Learning (DRL) Gym, utilizing TensorFlow for neural network computations. It's designed to demonstrate the principles of Deep Q-Learning by training an AI agent to navigate a grid environment towards a specified goal.  ## Detailed Article For a comprehensive, follow-along guide and theoretical background, refer to my Medium article: [Develop Your First AI Agent: Deep Q-Learning](#medium-article-link). ## Technologies Used - **Python** 3.11.6 - The programming language used. - **TensorFlow** 2.14.0 - An open-source machine learning library by Google that we’ll use to build and train our neural network. ## Features - Customizable grid-based environment. - Deep Q-Learning implementation for efficient learning. - Experience replay to enhance the learning process. - Adjustable parameters for experimentation. ## Contact Name - Heston Vaughan Email - [heston.cv@gmail.com](mailto:heston.cv@gmail.com) Article Link: [Develop Your First AI Agent: Deep Q-Learning](#medium-article-link)
