masoudslipknot/Reinforcment_Learning_ValueIteration
Reinforcement- Learning project: Value Iteration Implementation.
This project helps demonstrate how an autonomous agent learns the best path to achieve goals in a grid-like environment. You provide a map with rewards for reaching certain spots and penalties for going out of bounds. The output is a 'policy map' that tells the agent the optimal direction to move from any given square. This is useful for anyone studying basic artificial intelligence or reinforcement learning algorithms.
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Use this if you are learning about or teaching the fundamentals of how an agent can learn optimal strategies in a simplified, grid-based world.
Not ideal if you need to apply reinforcement learning to complex, real-world problems with continuous states or actions.
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Java
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Feb 08, 2019
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