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ma-ppo-RBC3D-medium-v0

Reinforcement Learning
stable-baselines3
deep-reinforcement-learning
fluidgym
active-flow-control
fluid-dynamics
simulation
RBC3D-medium-v0
Eval Results (legacy)
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  1. .gitattributes +2 -0
  2. 0/eval_sequence_20000.csv +0 -0
  3. 0/eval_sequence_40000.csv +0 -0
  4. 0/eval_sequence_45000.csv +0 -0
  5. 0/eval_sequence_50000.csv +0 -0
  6. 0/test/processed_3d_temperature_test_eval_episode_0.gif +3 -0
  7. 0/test/pruned_3d_temperature_test_eval_episode_0.gif +3 -0
  8. 0/test/test_eval_sequences.csv +0 -0
  9. README.md +6 -0
  10. replay.mp4 +2 -2
.gitattributes CHANGED
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  replay.mp4 filter=lfs diff=lfs merge=lfs -text
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README.md CHANGED
@@ -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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  ```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)
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