safe-autonomous-systems / ma-ppo-TCFLarge3D-both-easy-v0

huggingface.co
Total runs: 20
24-hour runs: 1
7-day runs: 11
30-day runs: 4
Model's Last Updated: February 04 2026
reinforcement-learning

Introduction of ma-ppo-TCFLarge3D-both-easy-v0

Model Details of ma-ppo-TCFLarge3D-both-easy-v0

PPO on TCFLarge3D-both-easy-v0 (FluidGym)

This repository is part of the FluidGym benchmark results. It contains trained Stable Baselines3 agents for the specialized TCFLarge3D-both-easy-v0 environment.

Evaluation Results
Global Performance (Aggregated across 5 seeds)

Mean Reward: 0.11 ± 0.07

Per-Seed Statistics
Run Mean Reward Std Dev
Seed 0 0.12 0.05
Seed 1 0.11 0.04
Seed 2 -0.01 0.04
Seed 3 0.19 0.06
Seed 4 0.15 0.05
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")

Runs of safe-autonomous-systems ma-ppo-TCFLarge3D-both-easy-v0 on huggingface.co

20
Total runs
1
24-hour runs
1
3-day runs
11
7-day runs
4
30-day runs

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ma-ppo-TCFLarge3D-both-easy-v0 install

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