Welcome to the Reinforcement Learning Repository!
Reinforcement learning is a branch of artificial intelligence concerned with learning how to make sequences of decisions. Inspired by the way humans learn from trial and error, reinforcement learning has led to significant advancements in various fields, including robotics, gaming, finance, and more. From training computers to play games like chess and Go at a superhuman level to optimizing complex industrial processes, reinforcement learning continues to push the boundaries of what's possible in AI.
This repository is a collection of Jupyter notebooks covering fundamental concepts like Markov decision processes and the Bellman equation, as well as advanced algorithms like Q-learning, deep Q-networks (DQN), and policy gradients. These notebooks document my journey of learning reinforcement learning concepts and algorithms, and I hope they will be valuable resources for anyone seeking to understand and apply reinforcement learning in their projects.
Whether you're a beginner exploring the basics or an experienced practitioner looking to deepen your knowledge, I believe these notebooks will help you grasp the essential concepts and algorithms of reinforcement learning.
Happy learning, and I hope you find these resources helpful on your reinforcement learning journey!
[Dang Nha/Reinforcement Learning Newbie]
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MIT License
Copyright (c) [2024] [Dang-Nha Nguyen]
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.