safe-autonomous-systems/ppo-RBC2D-easy-v0

PPO on RBC2D-easy-v0 (FluidGym)

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

Evaluation Results

Global Performance (Aggregated across 5 seeds)

Mean Reward: 0.87 ± 0.03

Per-Seed Statistics

Run	Mean Reward	Std Dev
Seed 0	0.87	0.12
Seed 1	0.83	0.13
Seed 2	0.90	0.13
Seed 3	0.91	0.11
Seed 4	0.86	0.12

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")

References

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