Metadata-Version: 2.1
Name: pyrorl
Version: 0.0.1
Summary: An RL OpenAI Gym Environment for Wildfire Evacuation
Home-page: https://github.com/sisl/wildfire
Author: Celtics Big 3
Author-email: clpondoc@stanford.edu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown

# Wildfire Evacuation RL Gym Environment

A new reinforcement learning OpenAI Gym environment built for the simulation of wildfire evacuation. Check out the [docs](https://sisl.github.io/wildfire/) and the [demo](https://www.loom.com/share/39ddd19c790a49c0a1ea7e13cd4d1005?sid=679b631a-74b7-41e3-bd88-3e7d14c0adc2).

## How To Use

First, install our package:

```bash
pip install wildfire-evac
```

To use our wildfire evacuation environment, define the dimensions of your grid, where the populated areas are, the paths, and which populated areas can use which path. See an example below.

```python
# Create environment
kwargs = {
    'num_rows': num_rows,
    'num_cols': num_cols,
    'populated_areas': populated_areas,
    'paths': paths,
    'paths_to_pops': paths_to_pops
}
env = gymnasium.make('wildfire_evac/WildfireEvacuation-v0', **kwargs)

# Run a simple loop of the environment
env.reset()
for _ in range(10):

    # Take action and observation
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)

    # Render environment and print reward
    env.render()
    print("Reward: " + str(reward))
```

For more examples, check out `examples/`.


