---
myst:
  substitutions:
    Colab: |-
      ```{image} ../_static/img/colab.svg
      ```
---

(quickstart)=

# Getting Started

:::{note}
You can try the following examples online using Google Colab {{ Colab }}
notebook: [RL Baselines zoo notebook]
:::

The hyperparameters for each environment are defined in
`hyperparameters/algo_name.yml`.

If the environment exists in this file, then you can train an agent
using:

```
python -m rl_zoo3.train --algo algo_name --env env_id
```

Or if you are in the RL Zoo3 folder:

```
python train.py --algo algo_name --env env_id
```

For example (with evaluation and checkpoints):

```
python -m rl_zoo3.train --algo ppo --env CartPole-v1 --eval-freq 10000 --save-freq 50000
```

If the trained agent exists, then you can see it in action using:

```
python -m rl_zoo3.enjoy --algo algo_name --env env_id
```

For example, enjoy A2C on Breakout during 5000 timesteps:

```
python -m rl_zoo3.enjoy --algo a2c --env BreakoutNoFrameskip-v4 --folder rl-trained-agents/ -n 5000
```

[rl baselines zoo notebook]: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/rl-baselines-zoo.ipynb