(enjoy)=

# Enjoy a Trained Agent

:::{note}
To download the repo with the trained agents, you must use
`git clone --recursive https://github.com/DLR-RM/rl-baselines3-zoo`
in order to clone the submodule too.
:::

## Enjoy a trained agent

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

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

For example, enjoy A2C on Breakout during 5000 timesteps:

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

If you have trained an agent yourself, you need to do:

```
# exp-id 0 corresponds to the last experiment, otherwise, you can specify another ID
python enjoy.py --algo algo_name --env env_id -f logs/ --exp-id 0
```

## Load Checkpoints, Best Model

To load the best model (when using evaluation environment):

```
python enjoy.py --algo algo_name --env env_id -f logs/ --exp-id 1 --load-best
```

To load a checkpoint (here the checkpoint name is
`rl_model_10000_steps.zip`):

```
python enjoy.py --algo algo_name --env env_id -f logs/ --exp-id 1 --load-checkpoint 10000
```

To load the latest checkpoint:

```
python enjoy.py --algo algo_name --env env_id -f logs/ --exp-id 1 --load-last-checkpoint
```

## Record a Video of a Trained Agent

Record 1000 steps with the latest saved model:

```
python -m rl_zoo3.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000
```

Use the best saved model instead:

```
python -m rl_zoo3.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000 --load-best
```

Record a video of a checkpoint saved during training (here the
checkpoint name is `rl_model_10000_steps.zip`):

```
python -m rl_zoo3.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000 --load-checkpoint 10000
```

## Record a Video of a Training Experiment

Apart from recording videos of specific saved models, it is also
possible to record a video of a training experiment where checkpoints
have been saved.

Record 1000 steps for each checkpoint, latest and best saved models:

```
python -m rl_zoo3.record_training --algo ppo --env CartPole-v1 -n 1000 -f logs --deterministic
```

The previous command will create a `mp4` file. To convert this file to
`gif` format as well:

```
python -m rl_zoo3.record_training --algo ppo --env CartPole-v1 -n 1000 -f logs --deterministic --gif
```