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@@ -4,7 +4,7 @@ tags:
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  - deep-q-network
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  - reinforcement-learning
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  - pathfinding
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- - hospital-floorplan
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  license: apache-2.0
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  datasets:
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  - custom
@@ -13,11 +13,11 @@ metrics:
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  - success_rate
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  ---
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- # Deep Q-Network for Hospital Floorplan Navigation
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  ## Model Description
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- This model is a Deep Q-Network (DQN) designed to find the most efficient path through a hospital floorplan for wheeling a bed without hitting obstacles. The model combines traditional pathfinding algorithms with reinforcement learning for optimal performance.
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  ## Model Architecture
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@@ -29,7 +29,7 @@ The model is a fully connected neural network with the following architecture:
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  ## Training
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  The model was trained using a hybrid approach:
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- 1. **A* Algorithm**: Initially, the A* algorithm was used to find the shortest path in a static environment.
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  2. **Reinforcement Learning**: The DQN was trained with guidance from the A* path to improve efficiency and adaptability.
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  ### Hyperparameters
@@ -41,7 +41,7 @@ The model was trained using a hybrid approach:
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  ## Usage
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- To use this model, load the saved state dictionary and initialize the DQN with the same architecture. The model can then be used to navigate a hospital floorplan and find the most efficient path to the target.
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  ### Example Code
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@@ -112,7 +112,7 @@ If you use this model in your research, please cite it as follows:
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  author = {Christopher Jones},
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  title = {Deep Q-Network for Floorplan Navigation},
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  year = {2024},
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- howpublished = {\url{https://huggingface.co/cajcodes/dqn-hospital-floorplan}},
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  note = {Accessed: YYYY-MM-DD}
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  }
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  ```
 
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  - deep-q-network
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  - reinforcement-learning
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  - pathfinding
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+ - floorplan
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  license: apache-2.0
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  datasets:
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  - custom
 
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  - success_rate
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  ---
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+ # Deep Q-Network for Floorplan Navigation
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  ## Model Description
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+ This model is a Deep Q-Network (DQN) designed to find the most efficient path through a floorplan without hitting obstacles. The model combines traditional pathfinding algorithms with reinforcement learning for optimal performance.
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  ## Model Architecture
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  ## Training
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  The model was trained using a hybrid approach:
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+ 1. **A(*) Algorithm**: Initially, the A* algorithm was used to find the shortest path in a static environment.
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  2. **Reinforcement Learning**: The DQN was trained with guidance from the A* path to improve efficiency and adaptability.
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  ### Hyperparameters
 
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  ## Usage
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+ To use this model, load the saved state dictionary and initialize the DQN with the same architecture. The model can then be used to navigate a floorplan and find the most efficient path to the target.
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  ### Example Code
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  author = {Christopher Jones},
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  title = {Deep Q-Network for Floorplan Navigation},
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  year = {2024},
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+ howpublished = {\url{https://huggingface.co/cajcodes/dqn-floorplan-finder}},
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  note = {Accessed: YYYY-MM-DD}
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  }
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  ```