| 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 | |
| predict_config: | |
| preview_file: replay.mp4 | |
| # 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) | |