safe-autonomous-systems/ma-sac-RBC2D-hard-v0

SAC on RBC2D-hard-v0 (FluidGym)

This repository is part of the FluidGym benchmark results. It contains trained Stable Baselines3 agents for the specialized RBC2D-hard-v0 environment.

Evaluation Results

Global Performance (Aggregated across 5 seeds)

Mean Reward: -0.69 ± 0.27

Per-Seed Statistics

Run	Mean Reward	Std Dev
Seed 0	-0.31	1.37
Seed 1	-0.46	3.00
Seed 2	-1.04	2.89
Seed 3	-0.82	2.59
Seed 4	-0.84	3.04

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