\page lpac LPAC Neural Network

\tableofcontents

We will organize files in a **workspace** directory: `${CoverageControl_ws}` (e.g., ~/CoverageControl\_ws).

Download and extract the file `lpac_CoverageControl.tar.gz` to the workspace directory.

The models folder already contains a trained LPAC model for a 1024x1024 environment with 32 robots, 32 features, and 128 communication radius.

There are two ways to classes for dataset generation located in `python/scripts/data_generation/`

1. `simple_data_generation.py`

The `simple_data_generation.py` is useful for generating a large dataset in parts and then combining them into a single dataset.

See `python/utils/process_data.sh` and `python/utils/dataset_utils.py` for tools to process and combine datasets.

To train the LPAC model, run the following command:

The second argument is the environment size, used to normalize the input features.

A sample `learning_params.toml` file is also provided in the `params` directory of the repository. See the file for details on the parameters.

There are two scripts for evaluation located in `python/scripts/evaluators/`

1. [eval_single_env.py](python/scripts/evaluators/eval_single_env.py)

2. [eval.py](python/scripts/evaluators/eval.py)