MehdiShahbazi/DQN-Mountain-Car-Gymnasium
This repo implements Deep Q-Network (DQN) for solving the Mountain Car v0 environment (discrete version) of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 with a custom reward function for faster convergence.
This project helps demonstrate how to train an AI agent to solve complex navigation problems where immediate actions aren't enough for success. It takes in a simulated environment's state and outputs the optimal sequence of actions for the agent to achieve its goal. This is designed for researchers and students exploring deep reinforcement learning techniques.
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Use this if you are studying or implementing Deep Q-Networks (DQN) and want a clear example of how to tackle environments requiring long-term strategic decision-making.
Not ideal if you are looking for a plug-and-play solution for a real-world control system without understanding the underlying reinforcement learning principles.
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Python
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MIT
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Last pushed
Mar 19, 2024
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