MehdiShahbazi/DQN-Frozenlake-Gymnasium
This repo implements Deep Q-Network (DQN) for solving the Frozenlake-v1 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 in both 4x4 and 8x8 map sizes.
This project helps demonstrate how a computer agent can learn to navigate a simple environment, like moving across a frozen lake to a goal, without falling into holes. It takes the agent's current location as input and produces the best next move. This is for anyone interested in understanding the basics of how artificial intelligence learns optimal strategies through trial and error.
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Use this if you are learning about reinforcement learning and want to see a practical example of the Deep Q-Network (DQN) algorithm applied to a basic navigation problem.
Not ideal if you are looking for a plug-and-play solution for complex, real-world robotic navigation or game AI that requires highly optimized performance and stability.
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Mar 19, 2024
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