Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/freedisch/dqn
https://github.com/freedisch/dqn
Last synced: 19 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/freedisch/dqn
- Owner: Freedisch
- Created: 2024-08-02T09:26:42.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2024-08-02T11:41:19.000Z (6 months ago)
- Last Synced: 2024-08-02T14:29:36.816Z (6 months ago)
- Language: Python
- Size: 6.84 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Deep Q-Learning Hospital Navigation Agent
## Project Overview
This project implements a reinforcement learning agent using Deep Q-Learning to navigate a simulated hospital environment. The agent, representing a nurse assistant, must efficiently navigate a 5x5 grid to reach a medicine cabinet while avoiding collisions with doctors and nurses.
## Environment Description
- 5x5 grid representing a hospital floor
- Agent: Nurse assistant (🙂)
- Static objects: 5 Beds, 1 Medicine Cabinet, 1 Doctor, 1 Nurse
- Actions: Move Up, Down, Left, Right
- Rewards:
- Reaching Medicine Cabinet: +10
- Reaching a Bed: +1
- Colliding with Doctor or Nurse: -5
- Each step: -0.1 (to encourage efficiency)
- Termination Conditions:
- Reaching the Medicine Cabinet (success)
- Colliding with Doctor or Nurse (failure)
- Max steps reached (100 steps)
## Project Structure
- `HospitalNavigation_env.py`: Custom Gym environment implementation
- `train.py`: Script to train the DQN agent
- `play.py`: Script to run simulations with the trained agent
- `hospital_navigation_weights.h5f`: Saved weights of the trained model
- `requirements.txt`: List of required Python packages
## Installation
1. Clone this repository:
```
git clone https://github.com/freedisch/DQN.git
cd DQN
```
2. Install the required packages:
```
pip install -r requirements.txt
```
## Usage
1. To train the agent:
```
python train.py
```
2. To run a simulation with the trained agent:
```
python play.py
```
## Video Demonstration
[Link to 5-minute video demonstration](https://youtu.be/gFM__0xhhmg)
## Technologies Used
- Python 3.8+
- OpenAI Gym
- Keras
- Keras-RL
- TensorFlow
## Author
Thibaut Batale
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.