safe-autonomous-systems/ma-sac-RBC3D-hard-v0

SAC on RBC3D-hard-v0 (FluidGym)

This repository is part of the FluidGym benchmark results. It contains trained Stable Baselines3 agents for the specialized RBC3D-hard-v0 environment.

Evaluation Results

Global Performance (Aggregated across 5 seeds)

Mean Reward: 0.30 ± 0.09

Per-Seed Statistics

Run	Mean Reward	Std Dev
Seed 0	0.29	0.09
Seed 1	0.13	0.19
Seed 2	0.40	0.07
Seed 3	0.34	0.05
Seed 4	0.31	0.09

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 SAC
model = SAC.load("0/ckpt_latest.zip")

References

• Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
• FluidGym GitHub Repository