> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> Integrate W&B with Stable Baselines3 to track reinforcement learning experiments and log training performance.

# Stable Baselines 3 PyTorch

[Stable Baselines 3](https://github.com/DLR-RM/stable-baselines3) (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. W\&B's SB3 integration:

* Records metrics such as losses and episodic returns.
* Uploads videos of agents playing the games.
* Saves the trained model.
* Logs the model's hyperparameters.
* Logs the model gradient histograms.

Review an [example SB3 training run](https://wandb.ai/wandb/sb3/runs/1jyr6z10).

## Log your SB3 experiments

```python theme={null}
from wandb.integration.sb3 import WandbCallback

model.learn(..., callback=WandbCallback())
```

<Frame>
  <img src="https://mintcdn.com/wb-21fd5541/w-lBKSCruauC3-2f/images/integrations/stable_baselines_demo.gif?s=70461da6ea20b78c3af345d06ee86232" alt="Stable Baselines 3 training with W&B" width="2448" height="1544" data-path="images/integrations/stable_baselines_demo.gif" />
</Frame>

## WandbCallback Arguments

| 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                          |

## Basic example

The W\&B SB3 integration uses the logs output from TensorBoard to log your metrics

```python theme={null}
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()
```