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--- |
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library_name: stable-baselines3 |
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tags: |
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- reinforcement-learning |
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- stable-baselines3 |
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- deep-reinforcement-learning |
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- fluidgym |
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- active-flow-control |
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- fluid-dynamics |
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- simulation |
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- RBC2D-medium-v0 |
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model-index: |
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- name: PPO-RBC2D-medium-v0 |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: FluidGym-RBC2D-medium-v0 |
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type: fluidgym |
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metrics: |
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- type: mean_reward |
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value: 0.06 |
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name: mean_reward |
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--- |
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# PPO on RBC2D-medium-v0 (FluidGym) |
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This repository is part of the **FluidGym** benchmark results. It contains trained Stable Baselines3 agents for the specialized **RBC2D-medium-v0** environment. |
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## Evaluation Results |
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### Global Performance (Aggregated across 5 seeds) |
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**Mean Reward:** 0.06 ± 0.15 |
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### Per-Seed Statistics |
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| Run | Mean Reward | Std Dev | |
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| --- | --- | --- | |
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| Seed 0 | -0.06 | 1.56 | |
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| Seed 1 | 0.27 | 1.35 | |
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| Seed 2 | 0.21 | 0.93 | |
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| Seed 3 | -0.12 | 1.43 | |
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| Seed 4 | 0.00 | 1.38 | |
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## About FluidGym |
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FluidGym is a benchmark for reinforcement learning in active flow control. |
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## Usage |
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Each seed is contained in its own subdirectory. You can load a model using: |
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```python |
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from stable_baselines3 import PPO |
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model = PPO.load("0/ckpt_latest.zip") |
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**Important:** The models were trained using ```fluidgym==0.0.2```. In order to use |
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them with newer versions of FluidGym, you need to wrap the environment with a |
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`FlattenObservation` wrapper as shown below: |
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```python |
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import fluidgym |
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from fluidgym.wrappers import FlattenObservation |
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from stable_baselines3 import PPO |
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env = fluidgym.make("RBC2D-medium-v0") |
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env = FlattenObservation(env) |
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model = PPO.load("path_to_model/ckpt_latest.zip") |
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obs, info = env.reset(seed=42) |
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action, _ = model.predict(obs, deterministic=True) |
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obs, reward, terminated, truncated, info = env.step(action) |
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``` |
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## References |
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* [Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control](http://arxiv.org/abs/2601.15015) |
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* [FluidGym GitHub Repository](https://github.com/safe-autonomous-systems/fluidgym) |
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