# Google Smart Buildings Control



## Real World Data

Redacted for anonymity

from cloud storage.

## Documentation

Redacted for anonymity

## Getting Started

A great place to start is by reviewing the
[Soft Actor Critic Demo notebook](smart_control/notebooks/SAC_Demo.ipynb). This
notebook will walk you through:

1. Creating a gym-compatible Reinforcement Learning (RL) environment.

2. Visualizing the environment.

3. Training an agent using the
   [Tensorflow Agents Library](https://www.tensorflow.org/agents).

Alternatively, RL agents can be trained by running various scripts in the
"smart_control/reinforcement_learning/scripts" directory.

Before running notebooks or scripts, make sure to complete the setup
instructions linked below.

## Setup

The [Setup Guide](docs/setup.md) provides all the information you need to run
the code locally.

## Contributing

The [Contributor's Guide](docs/contributing.md) provides more information on how
to contribute to this repository.

## [License](LICENSE)
