safe-autonomous-systems/ppo-CylinderJet3D-easy-v0

PPO on CylinderJet3D-easy-v0 (FluidGym)

This repository is part of the FluidGym benchmark results. It contains trained Stable Baselines3 agents for the specialized CylinderJet3D-easy-v0 environment.

Evaluation Results

Global Performance (Aggregated across 3 seeds)

Mean Reward: -0.24 ± 0.07

Per-Seed Statistics

Run	Mean Reward	Std Dev
Seed 0	-0.16	0.28
Seed 1	-0.34	0.32
Seed 2	-0.21	0.28

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")

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:

import fluidgym
from fluidgym.wrappers import FlattenObservation
from stable_baselines3 import PPO

env = fluidgym.make("CylinderJet3D-easy-v0")
env = FlattenObservation(env)
model = PPO.load("path_to_model/ckpt_latest.zip")

obs, info = env.reset(seed=42)

action, _ = model.predict(obs, deterministic=True)
obs, reward, terminated, truncated, info = env.step(action)

References

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