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ma-ppo-RBC3D-easy-v0

Reinforcement Learning
stable-baselines3
deep-reinforcement-learning
fluidgym
active-flow-control
fluid-dynamics
simulation
RBC3D-easy-v0
Eval Results (legacy)
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README.md CHANGED
@@ -22,8 +22,8 @@ model-index:
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  - type: mean_reward
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  value: 0.35
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  name: mean_reward
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-
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-
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  ---
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  # PPO on RBC3D-easy-v0 (FluidGym)
@@ -52,3 +52,9 @@ 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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  - type: mean_reward
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  value: 0.35
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  name: mean_reward
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+ predict_config:
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+ preview_file: replay.mp4
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  ---
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  # PPO on RBC3D-easy-v0 (FluidGym)
 
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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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+ ```
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+
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+ ## References
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+
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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)