tirthajyoti/RL_basics
Basic Reinforcement Learning algorithms
This project helps you understand how an agent can learn optimal behavior in a simulated environment through trial and error. You provide a description of an environment (like a grid world) with possible actions and rewards, and it shows you how an agent can learn to achieve goals. This is useful for researchers or students exploring fundamental concepts in artificial intelligence and machine learning, particularly in sequential decision-making.
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Use this if you are studying or teaching the core mechanics of reinforcement learning algorithms and want to visualize how an agent learns.
Not ideal if you need to apply advanced reinforcement learning to real-world, complex problems or build production-ready AI systems.
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19
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13
Language
Jupyter Notebook
License
MIT
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Last pushed
Jun 06, 2019
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