README.md

---
library_name: stable-baselines3
tags:
- reinforcement-learning
- stable-baselines3
- deep-reinforcement-learning
- fluidgym
- active-flow-control
- fluid-dynamics
- simulation
- CylinderRot2D-medium-v0
model-index:
- name: PPO-CylinderRot2D-medium-v0
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: FluidGym-CylinderRot2D-medium-v0
      type: fluidgym
    metrics:
    - type: mean_reward
      value: 0.31
      name: mean_reward


---

# PPO on CylinderRot2D-medium-v0 (FluidGym)

This repository is part of the **FluidGym** benchmark results. It contains trained Stable Baselines3 agents for the specialized **CylinderRot2D-medium-v0** environment.

## Evaluation Results

### Global Performance (Aggregated across 5 seeds)
**Mean Reward:** 0.31 ± 0.03

### Per-Seed Statistics
| Run | Mean Reward | Std Dev |
| --- | --- | --- |
| Seed 0 | 0.31 | 0.64 |
| Seed 1 | 0.29 | 0.66 |
| Seed 2 | 0.26 | 0.55 |
| Seed 3 | 0.30 | 0.66 |
| Seed 4 | 0.36 | 0.63 |

## About FluidGym
FluidGym is a benchmark for reinforcement learning in active flow control.

## Usage
Each seed is contained in its own subdirectory. You can load a model using:
```python
from stable_baselines3 import PPO
model = PPO.load("0/ckpt_latest.zip")
```

**Important:** The models were trained using ```fluidgym==0.0.2```. In order to use
them with newer versions of FluidGym, you need to wrap the environment with a
`FlattenObservation` wrapper as shown below:
```python
import fluidgym
from fluidgym.wrappers import FlattenObservation
from stable_baselines3 import PPO

env = fluidgym.make("CylinderRot2D-medium-v0")
env = FlattenObservation(env)
model = PPO.load("path_to_model/ckpt_latest.zip")

obs, info = env.reset(seed=42)

action, _ = model.predict(obs, deterministic=True)
obs, reward, terminated, truncated, info = env.step(action)
```

## References

* [Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control](http://arxiv.org/abs/2601.15015)
* [FluidGym GitHub Repository](https://github.com/safe-autonomous-systems/fluidgym)