Stable Baselines 3
Stable Baselines 3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. W&B's SB3 integration will:
- Record metrics such as losses and episodic returns
- Upload videos of agents playing the games
- Save the trained model
- Log model's hyperparameters
- Log model gradient histograms
from wandb.integration.sb3 import WandbCallback
model.learn(..., callback=WandbCallback())
Argument | Usage |
|---|---|
verbose | The verbosity of sb3 output |
model_save_path | Path to the folder where the model will be saved, The default value is `None` so the model is not logged |
model_save_freq | Frequency to save the model |
gradient_save_freq | Frequency to log gradient. The default value is 0 so the gradients are not logged |
The W&B SB3 integration uses the logs output from TensorBoard to log your metrics
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.vec_env import DummyVecEnv, VecVideoRecorder
import wandb
from wandb.integration.sb3 import WandbCallback
config = {
"policy_type": "MlpPolicy",
"total_timesteps": 25000,
"env_name": "CartPole-v1",
}
run = wandb.init(
project="sb3",
config=config,
sync_tensorboard=True, # auto-upload sb3's tensorboard metrics
monitor_gym=True, # auto-upload the videos of agents playing the game
save_code=True, # optional
)
def make_env():
env = gym.make(config["env_name"])
env = Monitor(env) # record stats such as returns
return env
env = DummyVecEnv([make_env])
env = VecVideoRecorder(env, f"videos/{run.id}", record_video_trigger=lambda x: x % 2000 == 0, video_length=200)
model = PPO(config["policy_type"], env, verbose=1, tensorboard_log=f"runs/{run.id}")
model.learn(
total_timesteps=config["total_timesteps"],
callback=WandbCallback(
gradient_save_freq=100,
model_save_path=f"models/{run.id}",
verbose=2,
),
)
run.finish()
Last modified 9mo ago