| Name | DroneNavigationGym-RL JSON |
| Version |
1.1.0
JSON |
| download |
| home_page | None |
| Summary | Autonomous Drone Navigation for Surveillance Gym Environment |
| upload_time | 2024-06-18 21:35:44 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | MIT License Copyright (c) [2024] [dtungpka] Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
drone
navigation
gym
reinforcement-learning
surveillance
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Autonomus Drone Navigation for Surveillance Environment
### Environments
This repository contains the implementation of Gym environment for the drone navigation in surveillance environment by [dtungpka](https://github.com/dtungpka)
>Drone-v0
### Installation
```bash
python3 -m pip install DroneNavigationGym-RL
```
### Usage
```python
import gymnasium as gym
import Autonomus_Drones_Navigation_For_Surveillance
env = gym.make('Drone-v0',drones=2,render_mode='human',size=20,targets=2,obstacles=2,battery=100)
obs, info = env.reset()
while True:
action = env.action_space.sample()
obs, reward, terminated, _, info = env.step(action)
if terminated:
break
env.close()
```
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