# Ensure your experiments are reproducible¶

Ensure the reproducibility of your experiments with comprehensive launcher files. Launcher files are used to initialize the algorithm, its components (policy, replay buffer, etc.), and tools for managing experiments.

There are some important details in this example launcher file.

from garage import wrap_experiment
from garage.experiment import deterministic
from garage.trainer import Trainer
import a.b.c

@wrap_experiment(wrap_experiment_args, wrap_experiment_kwargs)
def experiment(ctxt=None, seed):
deterministic.set_seed(seed)
trainer = Trainer(snapshot_config=ctxt)
env = YOUR_ENV

# Initialize algorithm dependencies (policy, Q-functions,
# replay buffers, etc.)
policy = ...
algo = ...

trainer.setup(algo=algo, env=env)
trainer.train(n_epochs=num_epochs, batch_size=)

seed = 0
experiment(seed)


## Important Take Aways¶

### Use garage.wrap_experiment for snapshotting and saving results¶

Use the function decorator wrap_experiment to enable the storage of results of your experiment. By default, experiments are named based on the name of the function that is wrapped by the wrap experiment decorator. In the case of the example above, experiments would be saved under the directory name experiment/. You can specify the directory in which all results are saved to using the parameter log_dir. You can further modify the directory tree under which your experiments are saved and the experiment naming notation by using the parameters prefix, name , use_existing_dir and name_parameters. For example, by setting name_parameters to true, the parameters to the function wrapped by wrap_experiment will be incorporated into the directory name. Using the above example launcher file, experiments would be saved under the directory name experiment_seed_*SEED_NUMBER*. There are defaults to these parameters that allow for easy to understand organization for most use cases. The logs for your experiments (csv’s, textual outputs, tensorboard logs) will be saved in this file structure. Lastly, you can save the repo that you launched your experiment from by enabling archive_launch_repo. This is enabled by default, and your archived repository is saved under the directory tree.

wrap_experiment’s other use is for enabling the snapshotting of your experiment. Snapshots are pkl files that can be used for retrieving the components of an experiment (policy, algorithm, sampler). They are used to capture the state of your experiment at any training epoch. You can control the rate at which you collect snapshots for your experiment using the parameter snapshot_mode. The snapshot_mode can be either “all” (all iterations will be saved), “last” (only the last iteration will be saved), “gap” (every snapshot_gap iterations are saved), or “none” (do not save snapshots).

### Use deterministic.set_seed¶

In machine learning research, setting the seed of the random number generator that is used by your algorithm and its components will allow you to reproduce the behavior of your algorithm. If you set the seed to be the same value on different runs, the behavior of your algorithm will be the same. Vice versa, setting the seed of your experiment to different numbers will allow you to observe the different behavior modes of your algorithm and its components under different sequences of random numbers. Note: In experiments using Pytorch+GPU, there is a small runtime performance cost for using deterministic.set_seed

### Use garage.trainer.Trainer for managing your training process¶

Trainer manages the boiler-plate interactions between your sampler, algorithm, logger, and snapshotter.

To setup the Trainer use the function Trainer.setup(algo, env, ...). It takes your algorithm and environment as required parameters. You can also control the sampling process you would like to use by specifying information such as the sampler_cls, worker_cls, etc. Lastly, to trigger the training process of your experiment, use the function Trainer.train(n_epochs, batch_size, ...). The required parameter n_epochs is used for specifying the number of training epochs that your experiment’s algorithm is run for. The parameter batch_size is used for specifying the default number of time steps that will be collected from your experiment’s environment between training epochs.

## Examples¶

For examples of how to write a launcher file for your experiment, refer to the launcher files under the examples directory.