metadata
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
predict_config:
preview_file: replay.mp4
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:
from stable_baselines3 import PPO
model = PPO.load("0/ckpt_latest.zip")