TroddenSpade/Exhaustive-Reinforcement-Learning
Exhaustive Implementation of Algorithms, Key Papers, and Well-Known Problems of Reinforcement Leaning
This project offers a comprehensive collection of code implementations for various Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) algorithms. It provides practical examples that demonstrate how these algorithms operate within different environments, such as game simulations like Black Jack or CartPole. Developers, researchers, and students in AI or machine learning fields can use this resource to understand, learn, and implement foundational and advanced RL techniques.
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Use this if you are an AI or machine learning practitioner looking for well-documented, executable code examples of reinforcement learning algorithms and their applications in various simulated environments.
Not ideal if you are looking for a plug-and-play solution for a specific real-world problem or if you are not familiar with programming concepts.
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Jupyter Notebook
License
MIT
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Last pushed
Aug 21, 2022
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