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Running Experiments

One of the best ways to get a feel for deep RL is to run the algorithms and see how they perform on different tasks. The Spinning Up code library makes small-scale (local) experiments easy to do, and in this section, we'll discuss two ways to run them: either from the command line, or through function calls in scripts.

Spinning Up ships with spinup/, a convenient tool that lets you easily launch any algorithm (with any choices of hyperparameters) from the command line. It also serves as a thin wrapper over the utilities for watching trained policies and plotting, although we will not discuss that functionality on this page (for those details, see the pages on experiment outputs and plotting).

The standard way to run a Spinning Up algorithm from the command line is

python -m [algo name] [experiment flags]


python -m ppo --env Walker2d-v2 --exp_name walker

You Should Know

If you are using ZShell: ZShell interprets square brackets as special characters. Spinning Up uses square brackets in a few ways for command line arguments; make sure to escape them, or try the solution recommended here if you want to escape them by default.

Detailed Quickstart Guide

python -m ppo --exp_name ppo_ant --env Ant-v2 --clip_ratio 0.1 0.2
    --hid[h] [32,32] [64,32] --act torch.nn.Tanh --seed 0 10 20 --dt
    --data_dir path/to/data

runs PPO in the Ant-v2 Gym environment, with various settings controlled by the flags.

By default, the PyTorch version will run (except for with TRPO, since Spinning Up doesn't have a PyTorch TRPO yet). Substitute ppo with ppo_tf1 for the Tensorflow version.

clip_ratio, hid, and act are flags to set some algorithm hyperparameters. You can provide multiple values for hyperparameters to run multiple experiments. Check the docs to see what hyperparameters you can set (click here for the PPO documentation).

hid and act are special shortcut flags for setting the hidden sizes and activation function for the neural networks trained by the algorithm.

The seed flag sets the seed for the random number generator. RL algorithms have high variance, so try multiple seeds to get a feel for how performance varies.

The dt flag ensures that the save directory names will have timestamps in them (otherwise they don't, unless you set FORCE_DATESTAMP=True in spinup/

The data_dir flag allows you to set the save folder for results. The default value is set by DEFAULT_DATA_DIR in spinup/, which will be a subfolder data in the spinningup folder (unless you change it).

Save directory names are based on exp_name and any flags which have multiple values. Instead of the full flag, a shorthand will appear in the directory name. Shorthands can be provided by the user in square brackets after the flag, like --hid[h]; otherwise, shorthands are substrings of the flag (clip_ratio becomes cli). To illustrate, the save directory for the run with clip_ratio=0.1, hid=[32,32], and seed=10 will be:


To use a PyTorch version of an algorithm, run with

python -m [algo]_pytorch

To use a Tensorflow version of an algorithm, run with

python -m [algo]_tf1

If you run python -m [algo] without _pytorch or _tf1, the runner will look in spinup/ for which version it should default to for that algorithm.

Every hyperparameter in every algorithm can be controlled directly from the command line. If kwarg is a valid keyword arg for the function call of an algorithm, you can set values for it with the flag --kwarg. To find out what keyword args are available, see either the docs page for an algorithm, or try

python -m [algo name] --help

to see a readout of the docstring.

You Should Know

Values pass through eval() before being used, so you can describe some functions and objects directly from the command line. For example:

python -m ppo --env Walker2d-v2 --exp_name walker --act torch.nn.ELU

sets torch.nn.ELU as the activation function. (Tensorflow equivalent: run ppo_tf1 with --act tf.nn.elu.)

You Should Know

There's some nice handling for kwargs that take dict values. Instead of having to provide

--key dict(v1=value_1, v2=value_2)

you can give

--key:v1 value_1 --key:v2 value_2

to get the same result.

You can launch multiple experiments, to be executed in series, by simply providing more than one value for a given argument. (An experiment for each possible combination of values will be launched.)

For example, to launch otherwise-equivalent runs with different random seeds (0, 10, and 20), do:

python -m ppo --env Walker2d-v2 --exp_name walker --seed 0 10 20

Experiments don't launch in parallel because they soak up enough resources that executing several at the same time wouldn't get a speedup.

A few flags receive special treatment.

.. option:: --env, --env_name

    *string*. The name of an environment in the OpenAI Gym. All Spinning Up algorithms are implemented as functions that accept ``env_fn`` as an argument, where ``env_fn`` must be a callable function that builds a copy of the RL environment. Since the most common use case is Gym environments, though, all of which are built through ``gym.make(env_name)``, we allow you to just specify ``env_name`` (or ``env`` for short) at the command line, which gets converted to a lambda-function that builds the correct gym environment.

Some algorithm arguments are relatively long, and we enabled shortcuts for them:

.. option:: --hid, --ac_kwargs:hidden_sizes

    *list of ints*. Sets the sizes of the hidden layers in the neural networks (policies and value functions).

.. option:: --act, --ac_kwargs:activation

    *tf op*. The activation function for the neural networks in the actor and critic.

These flags are valid for all current Spinning Up algorithms.

These flags are not hyperparameters of any algorithm, but change the experimental configuration in some way.

.. option:: --cpu, --num_cpu

    *int*. If this flag is set, the experiment is launched with this many processes, one per cpu, connected by MPI. Some algorithms are amenable to this sort of parallelization but not all. An error will be raised if you try setting ``num_cpu`` > 1 for an incompatible algorithm. You can also set ``--num_cpu auto``, which will automatically use as many CPUs as are available on the machine.

.. option:: --exp_name

    *string*. The experiment name. This is used in naming the save directory for each experiment. The default is "cmd" + [algo name].

.. option:: --data_dir

    *path*. Set the base save directory for this experiment or set of experiments. If none is given, the ``DEFAULT_DATA_DIR`` in ``spinup/`` will be used.

.. option:: --datestamp

    *bool*. Include date and time in the name for the save directory of the experiment.

Results for a particular experiment (a single run of a configuration of hyperparameters) are stored in



  • data_dir is the value of the --data_dir flag (defaults to DEFAULT_DATA_DIR from spinup/ if --data_dir is not given),
  • the outer_prefix is a YY-MM-DD_ timestamp if the --datestamp flag is raised, otherwise nothing,
  • the inner_prefix is a YY-MM-DD_HH-MM-SS- timestamp if the --datestamp flag is raised, otherwise nothing,
  • and suffix is a special string based on the experiment hyperparameters.

Suffixes are only included if you run multiple experiments at once, and they only include references to hyperparameters that differ across experiments, except for random seed. The goal is to make sure that results for similar experiments (ones which share all params except seed) are grouped in the same folder.

Suffixes are constructed by combining shorthands for hyperparameters with their values, where a shorthand is either 1) constructed automatically from the hyperparameter name or 2) supplied by the user. The user can supply a shorthand by writing in square brackets after the kwarg flag.

For example, consider:

python -m ddpg_tf1 --env Hopper-v2 --hid[h] [300] [128,128] --act tf.nn.tanh tf.nn.relu

Here, the --hid flag is given a user-supplied shorthand, h. The --act flag is not given a shorthand by the user, so one will be constructed for it automatically.

The suffixes produced in this case are:


Note that the h was given by the user. the ac-act shorthand was constructed from ac_kwargs:activation (the true name for the act flag).

You Don't Actually Need to Know This One

Each individual algorithm is located in a file spinup/algos/BACKEND/ALGO_NAME/, and these files can be run directly from the command line with a limited set of arguments (some of which differ from what's available to spinup/ The command line support in the individual algorithm files is essentially vestigial, however, and this is not a recommended way to perform experiments.

This documentation page will not describe those command line calls, and will only describe calls through spinup/

Each algorithm is implemented as a python function, which can be imported directly from the spinup package, eg

>>> from spinup import ppo_pytorch as ppo

See the documentation page for each algorithm for a complete account of possible arguments. These methods can be used to set up specialized custom experiments, for example:

from spinup import ppo_tf1 as ppo
import tensorflow as tf
import gym

env_fn = lambda : gym.make('LunarLander-v2')

ac_kwargs = dict(hidden_sizes=[64,64], activation=tf.nn.relu)

logger_kwargs = dict(output_dir='path/to/output_dir', exp_name='experiment_name')

ppo(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=5000, epochs=250, logger_kwargs=logger_kwargs)

It's often useful in machine learning research to run the same algorithm with many possible hyperparameters. Spinning Up ships with a simple tool for facilitating this, called ExperimentGrid.

Consider the example in spinup/examples/pytorch/

 from spinup.utils.run_utils import ExperimentGrid
 from spinup import ppo_pytorch
 import torch

 if __name__ == '__main__':
     import argparse
     parser = argparse.ArgumentParser()
     parser.add_argument('--cpu', type=int, default=4)
     parser.add_argument('--num_runs', type=int, default=3)
     args = parser.parse_args()

     eg = ExperimentGrid(name='ppo-pyt-bench')
     eg.add('env_name', 'CartPole-v0', '', True)
     eg.add('seed', [10*i for i in range(args.num_runs)])
     eg.add('epochs', 10)
     eg.add('steps_per_epoch', 4000)
     eg.add('ac_kwargs:hidden_sizes', [(32,), (64,64)], 'hid')
     eg.add('ac_kwargs:activation', [torch.nn.Tanh, torch.nn.ReLU], ''), num_cpu=args.cpu)

(An equivalent Tensorflow example is available in spinup/examples/tf1/

After making the ExperimentGrid object, parameters are added to it with

eg.add(param_name, values, shorthand, in_name)

where in_name forces a parameter to appear in the experiment name, even if it has the same value across all experiments.

After all parameters have been added,, **run_kwargs)

runs all experiments in the grid (one experiment per valid configuration), by providing the configurations as kwargs to the function thunk. uses a function named call_experiment to launch thunk, and **run_kwargs specify behaviors for call_experiment. See the documentation page for details.

Except for the absence of shortcut kwargs (you can't use hid for ac_kwargs:hidden_sizes in ExperimentGrid), the basic behavior of ExperimentGrid is the same as running things from the command line. (In fact, uses an ExperimentGrid under the hood.)