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| Run | Mean Reward | Std Dev |
|---|---|---|
| Seed 0 | 0.12 | 0.05 |
| Seed 1 | 0.11 | 0.04 |
| Seed 2 | -0.01 | 0.04 |
| Seed 3 | 0.19 | 0.06 |
| Seed 4 | 0.15 | 0.05 |
1from stable_baselines3 import PPO
2model = PPO.load("0/ckpt_latest.zip")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:1import fluidgym
2from fluidgym.wrappers import FlattenObservation
3from stable_baselines3 import PPO
4
5env = fluidgym.make("TCFLarge3D-both-easy-v0")
6env = FlattenObservation(env)
7model = PPO.load("path_to_model/ckpt_latest.zip")
8
9obs, info = env.reset(seed=42)
10
11action, _ = model.predict(obs, deterministic=True)
12obs, reward, terminated, truncated, info = env.step(action)