This repository contains hydra sweeper versions of proven AutoRL tuning tools for plug-and-play use. Currently included:

• Differential Evolution Hyperband
• Standard Population Based Training (with warmstarting option)
• Population Based Bandits (with Mix/Multi versions and warmstarting option)
• Bayesian-Generational Population Based Training

We recommend starting in the examples directory to see how the sweepers work. Assume that everything here is a minimizer! You can maximize instead by passing 'maximize=true' as a sweeper kwarg. For more background information, see here.

We recommend creating a conda environment to install the sweeper in. Choose which you want to use and install the dependencies for these sweepers. For all available options, use 'all' or:

conda create -n autorl-sweepers python==3.9
conda activate autorl-sweepers
pip install -e .[dehb,pb2,bgt]

If you want to work on the code itself, you can also use:

make install-dev

In 'examples' you can see example configurations and setups for all sweepers on Stable Baselines 3 agents. To run an example with the sweeper, you need to set the '--multirun' flag:

python examples/dehb_for_pendulum_ppo.py -m

In your yaml-configuration file, set hydra/sweeper to the sweeper name, e.g. DEHB:

defaults:
  - override hydra/sweeper: DEHB

You can also add hydra/sweeper=<sweeper_name> to your command line. The sweepers will only be found if the hydra_plugins directory is in your PYTHONPATH. You can check if it's loaded by running your script with --info plugins.

The definition of the hyperparameters is based on ConfigSpace. The syntax of the hyperparameters is according to ConfigSpace's json serialization. Please see their user guide for more information on how to configure hyperparameters.

Your yaml-configuration file must adhere to following syntax:

hydra:
  sweeper:
    ...
    search_space:
      hyperparameters:  # required
        hyperparameter_name_0:
          ...
        hyperparameter_name_1:
          ...
        ...

The configspace fields conditions and forbiddens aren't implemented for the solvers (except possibly DEHB?). Please don't use them, they'll be ignored.

Defining a uniform integer parameter is easy:

n_neurons:
  type: uniform_int  # or have a float parameter by specifying 'uniform_float'
  lower: 8
  upper: 1024
  log: true  # optimize the hyperparameter in log space
  default_value: ${n_neurons}  # you can set your default value to the one normally used in your config

Same goes for categorical parameters:

activation:
  type: categorical
  choices: [logistic, tanh, relu]
  default_value: ${activation}

See the CONTRIBUTING file for how to help out.

This package is Apache 2.0 licensed, as found in the LICENSE file.