* Add class ExperimentCollection to improve usability
* Remove parameters from ExperimentBuilder.build
* Renamed ExperimentBuilder.build_default_seeded_experiments to build_seeded_collection,
changing the return type to ExperimentCollection
* Replace temp_config_mutation (which was not appropriate for the public API) with
method copy (which performs a safe deep copy)
# Changes
## Dependencies
- New extra "eval"
## Api Extension
- `Experiment` and `ExperimentConfig` now have a `name`, that can
however be overridden when `Experiment.run()` is called
- When building an `Experiment` from an `ExperimentConfig`, the user has
the option to add info about seeds to the name.
- New method in `ExperimentConfig` called
`build_default_seeded_experiments`
- `SamplingConfig` has an explicit training seed, `test_seed` is
inferred.
- New `evaluation` package for repeating the same experiment with
multiple seeds and aggregating the results (important extension!).
Currently in alpha state.
- Loggers can now restore the logged data into python by using the new
`restore_logged_data`
## Breaking Changes
- `AtariEnvFactory` (in examples) now receives explicit train and test
seeds
- `EnvFactoryRegistered` now requires an explicit `test_seed`
- `BaseLogger.prepare_dict_for_logging` is now abstract
---------
Co-authored-by: Maximilian Huettenrauch <m.huettenrauch@appliedai.de>
Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
Co-authored-by: Michael Panchenko <35432522+MischaPanch@users.noreply.github.com>
* EnvFactory now uses the creation of a single environment as
the basic functionality which the more high-level functions build
upon
* Introduce enum EnvMode to indicate the purpose for which an env
is created, allowing the factory creation process to change its
behaviour accordingly
* Add EnvFactoryGymnasium to provide direct support for envs that
can be created via gymnasium.make
- EnvPool is supported via an injectible EnvPoolFactory
- Existing EnvFactory implementations are now derived from
EnvFactoryGymnasium
* Use a separate environment (which uses new EnvMode.WATCH) for
watching agent performance after training (instead of using test
environments, which the user may want to configure differently)
of number of environments in SamplingConfig is used
(values are now passed to factory method)
This is clearer and removes the need to pass otherwise
unnecessary configuration to environment factories at
construction
* Add persistence/restoration of Experiment instance
* Add file logging in experiment
* Allow all persistence/logging to be disabled
* Disable persistence in tests
