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

Name: sac-RBC2D-hard-v0
Availability: InStock

stable-baselines3

reinforcement-learning

deep-reinforcement-learning

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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: 0.51 ± 0.08

Per-Seed Statistics

Run	Mean Reward	Std Dev
Seed 0	0.53	1.09
Seed 1	0.40	1.48
Seed 2	0.62	0.91
Seed 3	0.56	1.65
Seed 4	0.44	1.96

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:

python

from stable_baselines3 import SAC
model = SAC.load("0/ckpt_latest.zip")

Important: The models were trained using fluidgym==0.0.2. In order to use them with newer versions of FluidGym, you need to wrap the environment with a FlattenObservation wrapper as shown below: