(plot)=

# Plot Scripts

Plot scripts (to be documented, see "Results" sections in SB3
documentation):

- `scripts/all_plots.py`/`scripts/plot_from_file.py` for plotting evaluations
- `scripts/plot_train.py` for plotting training reward/success

## Examples

Plot training success (y-axis) w.r.t. timesteps (x-axis) with a moving
window of 500 episodes for all the `Fetch` environment with `HER`
algorithm:

```
python scripts/plot_train.py -a her -e Fetch -y success -f rl-trained-agents/ -w 500 -x steps
```

Plot evaluation reward curve for TQC, SAC and TD3 on the HalfCheetah and
Ant PyBullet environments:

```
python3 scripts/all_plots.py -a sac td3 tqc --env HalfCheetahBullet AntBullet -f rl-trained-agents/
```

## Plot with the rliable library

The RL zoo integrates some of
[rliable](https://agarwl.github.io/rliable/) library features. You
can find a visual explanation of the tools used by rliable in this [blog
post](https://araffin.github.io/post/rliable/).

First, you need to install
[rliable](https://github.com/google-research/rliable).

Note: Python 3.7+ is required in that case.

Then export your results to a file using the `all_plots.py` script
(see above):

```
python scripts/all_plots.py -a sac td3 tqc --env Half Ant -f logs/ -o logs/offpolicy
```

You can now use the `plot_from_file.py` script with `--rliable`,
`--versus` and `--iqm` arguments:

```
python scripts/plot_from_file.py -i logs/offpolicy.pkl --skip-timesteps --rliable --versus -l SAC TD3 TQC
```

:::{note}
you may need to edit `plot_from_file.py`, in particular the
`env_key_to_env_id` dictionary and the
`scripts/score_normalization.py` which stores min and max score for
each environment.
:::

Remark: plotting with the `--rliable` option is usually slow as
confidence intervals need to be computed using bootstrap sampling.