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wfcrl-benchmark

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Benchmark of state-of-the-art reinforcement learning algorithms for wind farm control

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## WFCRL Algorithms

This repository contains the source code for the WFCRL multi-agent RL benchmark.
The benchmark is done on the [WFCRL environment suite](https://github.com/ifpen/wfcrl-env).

All experiments are adapted from the [CleanRL](https://github.com/vwxyzjn/cleanrl) repository.
Algorithms:

| **Algorithm**        | **File** | **Description**     |
|----------------------------------|--------------------|--------------------------------------------------------------------------------------|
| IPPO           | `algos/baseline_ippo.py`   | See [Yu et. al](https://arxiv.org/abs/2103.01955)            |
| MAPPO          | `algos/baseline_mappo.py`  |  See [Yu et. al](https://arxiv.org/abs/2103.01955)     |
| QMIX           | `algos/baseline_qmix.py`     | See [Rashid et. al](https://arxiv.org/abs/1803.11485)  |
| IFAC           | `algos/ifac.py`     | Simple online actor critic with Fourier Basis     |
| IQN           | `algos/idqn.py`     | Simple independent DQN    |

Install the dependencies:
```
pip install -r requirements
```

Launch an IPPO training experiment on the `Dec_Ablaincourt_Floris` environment:

```
python algos/baseline_ippo.py --seed 1 --env_id Dec_Ablaincourt_Floris --total_timesteps 100000
```

Evaluate it on the on the `Dec_Ablaincourt_Fastfarm` environment:

```
mpiexec -n 1 python algos/eval.py --seed 0 --algo ippo --env_id Dec_Ablaincourt_Fastfarm --num_episodes 1 --pretrained_models path/to/run
```

Experiments for training and evaluation runs are in the `scripts` folder. Add `--scenario windrose` to train/eval on *Wind Scenario II*:

```
python algos/baseline_ippo.py --seed 1 --env_id Dec_Ablaincourt_Floris --total_timesteps 1000000 --scenario windrose
```

To track the experiment in Wandb, add your API key in an `.env` file at the root of the folder:

```
WANDB_API_KEY=you_api_key
```

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