---
library_name: stable-baselines3
tags:
- reinforcement-learning
- stable-baselines3
- deep-reinforcement-learning
- fluidgym
- active-flow-control
- fluid-dynamics
- simulation
- RBC2D-medium-v0
model-index:
- name: PPO-RBC2D-medium-v0
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: FluidGym-RBC2D-medium-v0
      type: fluidgym
    metrics:
    - type: mean_reward
      value: 0.14
      name: mean_reward


---

# PPO on RBC2D-medium-v0 (FluidGym)

This repository is part of the **FluidGym** benchmark results. It contains trained Stable Baselines3 agents for the specialized **RBC2D-medium-v0** environment.

## Evaluation Results

### Global Performance (Aggregated across 5 seeds)
**Mean Reward:** 0.14 ± 0.07

### Per-Seed Statistics
| Run | Mean Reward | Std Dev |
| --- | --- | --- |
| Seed 0 | 0.15 | 1.09 |
| Seed 1 | 0.02 | 1.32 |
| Seed 2 | 0.22 | 1.28 |
| Seed 3 | 0.12 | 1.35 |
| Seed 4 | 0.18 | 1.41 |

## 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")
```

## 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)