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---
library_name: stable-baselines3
tags:
- reinforcement-learning
- stable-baselines3
- deep-reinforcement-learning
- fluidgym
- active-flow-control
- fluid-dynamics
- simulation
- RBC2D-easy-v0
model-index:
- name: PPO-RBC2D-easy-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FluidGym-RBC2D-easy-v0
type: fluidgym
metrics:
- type: mean_reward
value: 0.87
name: mean_reward
---
# PPO on RBC2D-easy-v0 (FluidGym)
This repository is part of the **FluidGym** benchmark results. It contains trained Stable Baselines3 agents for the specialized **RBC2D-easy-v0** environment.
## Evaluation Results
### Global Performance (Aggregated across 5 seeds)
**Mean Reward:** 0.87 ± 0.03
### Per-Seed Statistics
| Run | Mean Reward | Std Dev |
| --- | --- | --- |
| Seed 0 | 0.87 | 0.12 |
| Seed 1 | 0.83 | 0.13 |
| Seed 2 | 0.90 | 0.13 |
| Seed 3 | 0.91 | 0.11 |
| Seed 4 | 0.86 | 0.12 |
## 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("RBC2D-easy-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)
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